Johan A. K. Suykens

491 papers A* 11A 6B 73C 21Misc 4Journal 287Unranked 85

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. IEEE Trans. Artif. Intell. Bram De Cooman, Johan A. K. Suykens
2025	A*	`conf`	Accelerating Spectral Clustering under Fairness Constraints. ICML Francesco Tonin, Alex Lambert, Johan A. K. Suykens, Volkan Cevher
2025	J	`jnl`	Accelerating Spectral Clustering under Fairness Constraints. CoRR Francesco Tonin, Alex Lambert, Johan A. K. Suykens, Volkan Cevher
2025	J	`jnl`	Generative Kernel Spectral Clustering. CoRR David Winant, Sonny Achten, Johan A. K. Suykens
2025	—	`conf`	HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity. ICANN (5) Sonny Achten, Zander Op de Beeck, Francesco Tonin, Volkan Cevher, Johan A. K. Suykens
2025	J	`jnl`	Learning to Drive Safely with Hybrid Options. CoRR Bram De Cooman, Johan A. K. Suykens
2025	J	`jnl`	Nonlinear functional regression by functional deep neural network with kernel embedding. J. Mach. Learn. Res. Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
2025	J	`jnl`	Rethinking PCA Through Duality. CoRR Jan Quan, Johan A. K. Suykens, Panagiotis Patrinos
2024	J	`jnl`	A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. CoRR Bram De Cooman, Johan A. K. Suykens
2024	J	`jnl`	Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. CoRR Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens
2024	J	`jnl`	Compressing Features for Learning With Noisy Labels. IEEE Trans. Neural Networks Learn. Syst. Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens
2024	J	`jnl`	Deep Kernel Principal Component Analysis for multi-level feature learning. Neural Networks Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens
2024	J	`jnl`	Explaining the model and feature dependencies by decomposition of the Shapley value. Decis. Support Syst. Joran Michiels, Johan A. K. Suykens, Maarten De Vos
2024	B	`conf`	Feature Learning using Multi-view Kernel Partial Least Squares. ESANN Xinjie Zeng, Qinghua Tao, Johan A. K. Suykens
2024	J	`jnl`	HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity. CoRR Sonny Achten, Francesco Tonin, Volkan Cevher, Johan A. K. Suykens
2024	J	`jnl`	Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. CoRR Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
2024	A*	`conf`	Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. ICML Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens
2024	J	`jnl`	Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. CoRR Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens
2024	J	`jnl`	Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
2024	A*	`conf`	Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. ICML Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
2024	J	`jnl`	Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. CoRR Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
2024	J	`jnl`	SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe. CoRR Joris Depoortere, Johan Driesen, Johan A. K. Suykens, Hussain Syed Kazmi
2024	C	`conf`	Sparsity via Sparse Group k-max Regularization. ACC Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens
2024	J	`jnl`	Sparsity via Sparse Group k-max Regularization. CoRR Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens
2024	J	`jnl`	Tensor-based multi-view spectral clustering via shared latent space. Inf. Fusion Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2024	A*	`conf`	Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification. AAAI Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	A Dual Formulation for Probabilistic Principal Component Analysis. CoRR Henri De Plaen, Johan A. K. Suykens
2023	J	`jnl`	Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems. CoRR Mihály Novák, Rocco Langone, Carlos Alzate, Johan A. K. Suykens
2023	J	`jnl`	CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities. CoRR Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan A. K. Suykens, Maarten De Vos
2023	—	`conf`	Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. PKDD/ECML Workshops (4) Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. CoRR Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	Deep Kernel Principal Component Analysis for Multi-level Feature Learning. CoRR Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	Duality in Multi-View Restricted Kernel Machines. CoRR Sonny Achten, Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
2023	J	`jnl`	Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. CoRR Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
2023	J	`jnl`	Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value. CoRR Joran Michiels, Maarten De Vos, Johan A. K. Suykens
2023	A*	`conf`	Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. ICML Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. CoRR Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens
2023	J	`jnl`	Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions. CoRR Joran Michiels, Maarten De Vos, Johan A. K. Suykens
2023	J	`jnl`	Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting. IEEE Trans. Evol. Comput. Konstantinos Theodorakos, Oscar Mauricio Agudelo, Joachim Schreurs, Johan A. K. Suykens, Bart De Moor
2023	J	`jnl`	Jigsaw-ViT: Learning jigsaw puzzles in vision transformer. Pattern Recognit. Lett. Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens
2023	J	`jnl`	Learning With Asymmetric Kernels: Least Squares and Feature Interpretation. IEEE Trans. Pattern Anal. Mach. Intell. Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
2023	J	`jnl`	Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs. CoRR Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens
2023	J	`jnl`	Multi-view Kernel PCA for Time series Forecasting. CoRR Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
2023	J	`jnl`	Multi-view kernel PCA for time series forecasting. Neurocomputing Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
2023	J	`jnl`	Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method. CoRR Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2023	A*	`conf`	Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. NeurIPS Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
2023	J	`jnl`	Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. CoRR Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
2023	J	`jnl`	Semi-Supervised Classification with Graph Convolutional Kernel Machines. CoRR Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2023	Misc	`conf`	Tensorized LSSVMS For Multitask Regression. ICASSP Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens
2023	J	`jnl`	Tensorized LSSVMs for Multitask Regression. CoRR Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens
2023	A*	`conf`	Unbalanced Optimal Transport: A Unified Framework for Object Detection. CVPR Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool
2023	J	`jnl`	Unbalanced Optimal Transport: A Unified Framework for Object Detection. CoRR Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool
2022	J	`jnl`	Compressing Features for Learning with Noisy Labels. CoRR Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens
2022	J	`jnl`	Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2022	J	`jnl`	Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2022	—	`conf`	Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies. LOD (2) Bram De Cooman, Johan A. K. Suykens, Andreas Ortseifen
2022	J	`jnl`	Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. CoRR Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens
2022	J	`jnl`	Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. CoRR Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
2022	J	`jnl`	Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2022	A*	`conf`	On the Double Descent of Random Features Models Trained with SGD. NeurIPS Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
2022	J	`jnl`	Piecewise Linear Neural Networks and Deep Learning. CoRR Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens
2022	J	`jnl`	Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. SIAM J. Math. Data Sci. Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens
2022	J	`jnl`	Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2022	B	`conf`	Recurrent Restricted Kernel Machines for Time-series Forecasting. ESANN Arun Pandey, Hannes De Meulemeester, Henri De Plaen, Bart De Moor, Johan A. K. Suykens
2022	J	`jnl`	Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network. IEEE Trans. Intell. Transp. Syst. Qinghua Tao, Zhen Li, Jun Xu, Shu Lin, Bart De Schutter, Johan A. K. Suykens
2022	J	`jnl`	Tensor-based Multi-view Spectral Clustering via Shared Latent Space. CoRR Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2022	J	`jnl`	Toward Deep Adaptive Hinging Hyperplanes. IEEE Trans. Neural Networks Learn. Syst. Qinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens
2022	J	`jnl`	Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. IEEE Trans. Pattern Anal. Mach. Intell. Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2021	J	`jnl`	A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach. J. Intell. Manuf. Kevin Villalobos, Johan A. K. Suykens, Arantza Illarramendi
2021	J	`jnl`	A novel neural grey system model with Bayesian regularization and its applications. Neurocomputing Xin Ma, Mei Xie, Johan A. K. Suykens
2021	J	`jnl`	Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. Mach. Learn. Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2021	—	`conf`	Boosting Co-Teaching With Compression Regularization for Label Noise. CVPR Workshops Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
2021	J	`jnl`	Boosting Co-teaching with Compression Regularization for Label Noise. CoRR Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
2021	J	`jnl`	Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2021	A	`conf`	Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens
2021	J	`jnl`	Generalization Properties of hyper-RKHS and its Applications. J. Mach. Learn. Res. Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2021	J	`jnl`	Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. Neural Networks Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
2021	—	`conf`	Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines. LOD Marcin Orchel, Johan A. K. Suykens
2021	C	`conf`	Kernel Machines in Time (Invited Talk). TIME Johan A. K. Suykens
2021	A	`conf`	Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2021	J	`jnl`	Latent Space Exploration Using Generative Kernel PCA. CoRR David Winant, Joachim Schreurs, Johan A. K. Suykens
2021	J	`jnl`	Learning with continuous piecewise linear decision trees. Expert Syst. Appl. Qinghua Tao, Zhen Li, Jun Xu, Na Xie, Shuning Wang, Johan A. K. Suykens
2021	—	`conf`	Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. LOD Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
2021	J	`jnl`	Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. CoRR Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
2021	—	`conf`	Neural Network Training as an Optimal Control Problem : - An Augmented Lagrangian Approach -. CDC Brecht Evens, Puya Latafat, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos
2021	J	`jnl`	On the Double Descent of Random Features Models Trained with SGD. CoRR Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
2021	J	`jnl`	Outlier detection in non-elliptical data by kernel MRCD. Stat. Comput. Joachim Schreurs, Iwein Vranckx, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw
2021	J	`jnl`	Tensor Network Kalman Filtering for Large-Scale LS-SVMs. CoRR Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier
2021	J	`jnl`	Tensor-based restricted kernel machines for multi-view classification. Inf. Fusion Lynn Houthuys, Johan A. K. Suykens
2021	—	`conf`	The Bures Metric for Generative Adversarial Networks. ECML/PKDD (2) Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
2021	J	`jnl`	Towards Deterministic Diverse Subset Sampling. CoRR Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
2021	B	`conf`	Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. IJCNN Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens
2021	J	`jnl`	Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. CoRR Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens
2021	J	`jnl`	Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Neural Networks Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2020	J	`jnl`	A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. IEEE Trans. Neural Networks Learn. Syst. Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens
2020	J	`jnl`	A Statistical Learning Approach to Modal Regression. J. Mach. Learn. Res. Yunlong Feng, Jun Fan, Johan A. K. Suykens
2020	A*	`conf`	A Theoretical Framework for Target Propagation. NeurIPS Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
2020	J	`jnl`	A Theoretical Framework for Target Propagation. CoRR Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
2020	J	`jnl`	Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2020	J	`jnl`	Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2020	J	`jnl`	Disentangled Representation Learning and Generation with Manifold Optimization. CoRR Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2020	J	`jnl`	Diversity sampling is an implicit regularization for kernel methods. CoRR Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2020	J	`jnl`	Efficient hinging hyperplanes neural network and its application in nonlinear system identification. Autom. Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang
2020	J	`jnl`	Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
2020	—	`conf`	Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. LOD (2) Marcin Orchel, Johan A. K. Suykens
2020	J	`jnl`	Generalizing Random Fourier Features via Generalized Measures. CoRR Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens
2020	J	`jnl`	Kernel regression in high dimension: Refined analysis beyond double descent. CoRR Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
2020	B	`conf`	Learning from partially labeled data. ESANN Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
2020	J	`jnl`	Outlier detection in non-elliptical data by kernel MRCD. CoRR Joachim Schreurs, Iwein Vranckx, Bart De Ketelaere, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw
2020	J	`jnl`	Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2020	A*	`conf`	Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens
2020	—	`conf`	Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality. LOD (1) Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
2020	J	`jnl`	Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality. CoRR Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
2020	J	`jnl`	The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. CoRR Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
2020	J	`jnl`	Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
2020	J	`jnl`	Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks Zahra Karevan, Johan A. K. Suykens
2020	J	`jnl`	Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. CoRR Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
2020	B	`conf`	Wasserstein Exponential Kernels. IJCNN Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
2020	J	`jnl`	Wasserstein Exponential Kernels. CoRR Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
2019	—	`conf`	Axiomatic Kernels on Graphs for Support Vector Machines. ICANN (Workshop) Marcin Orchel, Johan A. K. Suykens
2019	J	`jnl`	Deep convolutional learning for general early design stage prediction models. Adv. Eng. Informatics Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer
2019	J	`jnl`	Efficient hinging hyperplanes neural network and its application in nonlinear system identification. CoRR Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang
2019	J	`jnl`	Generative Restricted Kernel Machines. CoRR Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
2019	J	`jnl`	Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. Int. J. Control Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens
2019	J	`jnl`	Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. IEEE Trans. Neural Networks Learn. Syst. Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens
2019	—	`conf`	Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN David Winant, Joachim Schreurs, Johan A. K. Suykens
2019	—	`conf`	Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN (Selected Papers) David Winant, Joachim Schreurs, Johan A. K. Suykens
2019	J	`jnl`	Nyström landmark sampling and regularized Christoffel functions. CoRR Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
2019	J	`jnl`	Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. CoRR Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens
2019	J	`jnl`	Robust classification of graph-based data. Data Min. Knowl. Discov. Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
2019	J	`jnl`	Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization. J. Mach. Learn. Res. Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A. K. Suykens
2019	—	`conf`	Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
2019	—	`conf`	Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
2019	J	`jnl`	Two-stage Best-scored Random Forest for Large-scale Regression. CoRR Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens
2018	J	`jnl`	A two-experiment approach to Wiener system identification. Autom. Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens
2018	J	`jnl`	Classification With Truncated $\ell _{1}$ Distance Kernel. IEEE Trans. Neural Networks Learn. Syst. Xiaolin Huang, Johan A. K. Suykens, Shuning Wang, Joachim Hornegger, Andreas K. Maier
2018	J	`jnl`	Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. IEEE Trans. Neural Networks Learn. Syst. Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
2018	J	`jnl`	Correntropy Based Matrix Completion. Entropy Yuning Yang, Yunlong Feng, Johan A. K. Suykens
2018	J	`jnl`	Deep hybrid neural-kernel networks using random Fourier features. Neurocomputing Siamak Mehrkanoon, Johan A. K. Suykens
2018	J	`jnl`	Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. Adv. Eng. Informatics Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer
2018	—	`conf`	Fast Adaptive Hinging Hyperplanes. CDC Qinghua Tao, Jun Xu, Johan A. K. Suykens, Shuning Wang
2018	J	`jnl`	Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. CoRR Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
2018	B	`conf`	Generative Kernel PCA. ESANN Joachim Schreurs, Johan A. K. Suykens
2018	J	`jnl`	Hammerstein system identification through best linear approximation inversion and regularisation. Int. J. Control Ricardo Castro-Garcia, Koen Tiels, Oscar Mauricio Agudelo, Johan A. K. Suykens
2018	J	`jnl`	Improved Initialization for Nonlinear State-Space Modeling. CoRR Anna Marconato, Jonas Sjöberg, Johan A. K. Suykens, Johan Schoukens
2018	J	`jnl`	Indefinite kernel spectral learning. Pattern Recognit. Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
2018	J	`jnl`	Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens
2018	J	`jnl`	Modified Frank-Wolfe algorithm for enhanced sparsity in support vector machine classifiers. Neurocomputing Carlos M. Alaíz, Johan A. K. Suykens
2018	J	`jnl`	Multi-View Kernel Spectral Clustering. Inf. Fusion Lynn Houthuys, Rocco Langone, Johan A. K. Suykens
2018	J	`jnl`	Multi-View Least Squares Support Vector Machines Classification. Neurocomputing Lynn Houthuys, Rocco Langone, Johan A. K. Suykens
2018	J	`jnl`	Optimal Quadrature-Sparsification for Integral Operator Approximation. SIAM J. Sci. Comput. Bertrand Gauthier, Johan A. K. Suykens
2018	J	`jnl`	Parallelized Tensor Train Learning of Polynomial Classifiers. IEEE Trans. Neural Networks Learn. Syst. Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong
2018	J	`jnl`	Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms. Neurocomputing Xiaolin Huang, Lei Shi, Ming Yan, Johan A. K. Suykens
2018	J	`jnl`	Regularized Semipaired Kernel CCA for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. Siamak Mehrkanoon, Johan A. K. Suykens
2018	B	`conf`	Shallow and Deep Models for Domain Adaptation problems. ESANN Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens
2018	A	`conf`	Solving lp-norm regularization with tensor kernels. AISTATS Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens
2018	J	`jnl`	Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. CoRR Zahra Karevan, Johan A. K. Suykens
2018	—	`conf`	Tensor Learning in Multi-view Kernel PCA. ICANN (2) Lynn Houthuys, Johan A. K. Suykens
2018	J	`jnl`	Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting. Entropy Zahra Karevan, Johan A. K. Suykens
2018	A	`conf`	Valuing Knowledge, Information and Agency in Multi-agent Reinforcement Learning: A Case Study in Smart Buildings. AAMAS Hussain Syed Kazmi, Johan A. K. Suykens, Johan Driesen
2018	J	`jnl`	Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings. CoRR Hussain Syed Kazmi, Johan A. K. Suykens, Johan Driesen
2018	—	`conf`	Weighted Multi-view Deep Neural Networks for Weather Forecasting. ICANN (3) Zahra Karevan, Lynn Houthuys, Johan A. K. Suykens
2017	—	`conf`	A primal-dual line search method and applications in image processing. EUSIPCO Pantelis Sopasakis, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos
2017	J	`jnl`	Deep Restricted Kernel Machines Using Conjugate Feature Duality. Neural Comput. Johan A. K. Suykens
2017	J	`jnl`	Editorial: A Successful Year and Looking Forward to 2017 and Beyond. IEEE Trans. Neural Networks Learn. Syst. Haibo He, Robert Haas, Jun Fu, Barbara Hammer, Daniel W. C. Ho, Fakhri Karray, Dhireesha Kudithipudi, José Antonio Lozano, Teresa Bernarda Ludermir, Jacek Mandziuk, Stefano Melacci, Antonio Paiva, Hong Qiao, Alain Rakotomamonjy, Shiliang Sun, Johan A. K. Suykens, Meng Wang
2017	J	`jnl`	Fast kernel spectral clustering. Neurocomputing Rocco Langone, Johan A. K. Suykens
2017	—	`conf`	MIMO hammerstein system identification using LS-SVM and steady state time response. SSCI Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens
2017	J	`jnl`	Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers. CoRR Carlos M. Alaíz, Johan A. K. Suykens
2017	B	`conf`	Moving Least Squares Support Vector Machines for weather temperature prediction. ESANN Zahra Karevan, Yunlong Feng, Johan A. K. Suykens
2017	B	`conf`	Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting. IJCNN Lynn Houthuys, Zahra Karevan, Johan A. K. Suykens
2017	—	`conf`	On the identification of Wiener systems with polynomial nonlinearity. CDC Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens
2017	J	`jnl`	Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions. CoRR Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens
2017	B	`conf`	Probabilistic matrix factorization from quantized measurements. IJCNN Giulio Bottegal, Johan A. K. Suykens
2017	B	`conf`	Scalable Hybrid Deep Neural Kernel Networks. ESANN Siamak Mehrkanoon, Andreas Zell, Johan A. K. Suykens
2017	J	`jnl`	Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. IEEE Trans. Neural Networks Learn. Syst. Xiaolin Huang, Lei Shi, Johan A. K. Suykens
2017	J	`jnl`	Supervised aggregated feature learning for multiple instance classification. Inf. Sci. Rocco Langone, Johan A. K. Suykens
2017	—	`conf`	Unpaired multi-view kernel spectral clustering. SSCI Lynn Houthuys, Johan A. K. Suykens
2016	B	`conf`	Clustering from two data sources using a kernel-based approach with weight coupling. ESANN Lynn Houthuys, Rocco Langone, Johan A. K. Suykens
2016	B	`conf`	Clustering-based feature selection for black-box weather temperature prediction. IJCNN Zahra Karevan, Johan A. K. Suykens
2016	J	`jnl`	Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. CoRR Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
2016	J	`jnl`	Coordinate Descent Algorithm for Ramp Loss Linear Programming Support Vector Machines. Neural Process. Lett. Xiangming Xi, Xiaolin Huang, Johan A. K. Suykens, Shuning Wang
2016	B	`conf`	Denoised Kernel Spectral data Clustering. IJCNN Raghvendra Mall, Halima Bensmail, Rocco Langone, Carolina Varon, Johan A. K. Suykens
2016	C	`conf`	Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso. CIARP Marcelo Aliquintuy, Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens
2016	J	`jnl`	Efficient evolutionary spectral clustering. Pattern Recognit. Lett. Rocco Langone, Marc Van Barel, Johan A. K. Suykens
2016	—	`conf`	Efficient multiple scale kernel classifiers. IEEE BigData Rocco Langone, Johan A. K. Suykens
2016	J	`jnl`	Entropy-Based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis. Entropy Rocco Langone, Marc Van Barel, Johan A. K. Suykens
2016	J	`jnl`	Estimating the unknown time delay in chemical processes. Eng. Appl. Artif. Intell. Siamak Mehrkanoon, Yuri A. W. Shardt, Johan A. K. Suykens, Steven X. Ding
2016	J	`jnl`	Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee. Mach. Learn. Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens
2016	B	`conf`	Fast in-memory spectral clustering using a fixed-size approach. ESANN Rocco Langone, Raghvendra Mall, Vilen Jumutc, Johan A. K. Suykens
2016	—	`conf`	Hammerstein system identification using LS-SVM and steady state time response. ECC Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Koen Tiels, Johan A. K. Suykens
2016	J	`jnl`	Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery. Neural Comput. Yunlong Feng, Shao-Gao Lv, Hanyuan Hang, Johan A. K. Suykens
2016	J	`jnl`	Learning Theory Estimates with Observations from General Stationary Stochastic Processes. Neural Comput. Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
2016	J	`jnl`	Learning theory estimates with observations from general stationary stochastic processes. CoRR Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
2016	J	`jnl`	Magnetic Eigenmaps for Visualization of Directed Networks. CoRR Michaël Fanuel, Ángela Fernández, Carlos M. Alaíz, Johan A. K. Suykens
2016	J	`jnl`	Magnetic eigenmaps for community detection in directed networks. CoRR Michaël Fanuel, Carlos M. Alaíz, Johan A. K. Suykens
2016	B	`conf`	Multi-label semi-supervised learning using regularized kernel spectral clustering. IJCNN Siamak Mehrkanoon, Johan A. K. Suykens
2016	J	`jnl`	Parallelized Tensor Train Learning of Polynomial Classifiers. CoRR Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong
2016	J	`jnl`	Rank-1 Tensor Properties with Applications to a Class of Tensor Optimization Problems. SIAM J. Optim. Yuning Yang, Yunlong Feng, Xiaolin Huang, Johan A. K. Suykens
2016	J	`jnl`	Reweighted stochastic learning. Neurocomputing Vilen Jumutc, Johan A. K. Suykens
2016	J	`jnl`	Robust Classification of Graph-Based Data. CoRR Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
2016	J	`jnl`	Robust Gradient Learning With Applications. IEEE Trans. Neural Networks Learn. Syst. Yunlong Feng, Yuning Yang, Johan A. K. Suykens
2016	J	`jnl`	Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator. IEEE Trans. Neural Networks Learn. Syst. Yuning Yang, Yunlong Feng, Johan A. K. Suykens
2016	J	`jnl`	Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses. Neural Comput. Yunlong Feng, Yuning Yang, Xiaolin Huang, Siamak Mehrkanoon, Johan A. K. Suykens
2016	—	`conf`	Scalable Semi-supervised kernel spectral learning using random Fourier features. SSCI Siamak Mehrkanoon, Johan A. K. Suykens
2016	B	`conf`	Spatio-temporal feature selection for black-box weather forecasting. ESANN Zahra Karevan, Johan A. K. Suykens
2016	J	`jnl`	The effect of imposing 'fractional abundance constraints' onto the multilayer perceptron for sub-pixel land cover classification. Int. J. Appl. Earth Obs. Geoinformation Stien Heremans, Johan A. K. Suykens, Jos Van Orshoven
2016	—	`conf`	Wiener System Identification using Best Linear Approximation within the LS-SVM framework. LA-CCI Ricardo Castro-Garcia, Johan A. K. Suykens
2015	J	`jnl`	A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification. CoRR Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens
2015	B	`conf`	A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification. IJCNN Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens
2015	J	`jnl`	A Rank-One Tensor Updating Algorithm for Tensor Completion. IEEE Signal Process. Lett. Yuning Yang, Yunlong Feng, Johan A. K. Suykens
2015	J	`jnl`	A robust ensemble approach to learn from positive and unlabeled data using SVM base models. Neurocomputing Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2015	B	`conf`	Black-box modeling for temperature prediction in weather forecasting. IJCNN Zahra Karevan, Siamak Mehrkanoon, Johan A. K. Suykens
2015	J	`jnl`	Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee. CoRR Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens
2015	—	`conf`	Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification. SSCI Mandar Chandorkar, Raghvendra Mall, Oliver Lauwers, Johan A. K. Suykens, Bart De Moor
2015	B	`conf`	Hierarchical semi-supervised clustering using KSC based model. IJCNN Siamak Mehrkanoon, Oscar Mauricio Agudelo, Raghvendra Mall, Johan A. K. Suykens
2015	J	`jnl`	Higher order Matching Pursuit for Low Rank Tensor Learning. CoRR Yuning Yang, Siamak Mehrkanoon, Johan A. K. Suykens
2015	J	`jnl`	Identifying intervals for hierarchical clustering using the Gershgorin circle theorem. Pattern Recognit. Lett. Raghvendra Mall, Siamak Mehrkanoon, Johan A. K. Suykens
2015	—	`conf`	Incorporating Best Linear Approximation within LS-SVM-based Hammerstein System Identification. CDC Ricardo Castro-Garcia, Koen Tiels, Johan Schoukens, Johan A. K. Suykens
2015	J	`jnl`	Incremental multi-class semi-supervised clustering regularized by Kalman filtering. Neural Networks Siamak Mehrkanoon, Oscar Mauricio Agudelo, Johan A. K. Suykens
2015	—	`ch.`	KSC-net: Community Detection for Big Data Networks. Big Data - Algorithms, Analytics, and Applications Raghvendra Mall, Johan A. K. Suykens
2015	—	`ch.`	Kernel Methods. Handbook of Computational Intelligence Marco Signoretto, Johan A. K. Suykens
2015	J	`jnl`	Kernel Spectral Clustering and applications. CoRR Rocco Langone, Raghvendra Mall, Carlos Alzate, Johan A. K. Suykens
2015	B	`conf`	Kernel spectral document clustering using unsupervised precision-recall metrics. IJCNN Raghvendra Mall, Johan A. K. Suykens
2015	J	`jnl`	LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Eng. Appl. Artif. Intell. Rocco Langone, Carlos Alzate, Bart De Ketelaere, Jonas Vlasselaer, Wannes Meert, Johan A. K. Suykens
2015	J	`jnl`	Learning solutions to partial differential equations using LS-SVM. Neurocomputing Siamak Mehrkanoon, Johan A. K. Suykens
2015	J	`jnl`	Learning with the maximum correntropy criterion induced losses for regression. J. Mach. Learn. Res. Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, Johan A. K. Suykens
2015	J	`jnl`	Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering. IEEE Trans. Neural Networks Learn. Syst. Siamak Mehrkanoon, Carlos Alzate, Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2015	J	`jnl`	Noise Level Estimation for Model Selection in Kernel PCA Denoising. IEEE Trans. Neural Networks Learn. Syst. Carolina Varon, Carlos Alzate, Johan A. K. Suykens
2015	J	`jnl`	Pinball Loss Minimization for One-bit Compressive Sensing. CoRR Xiaolin Huang, Lei Shi, Ming Yan, Johan A. K. Suykens
2015	B	`conf`	Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering. ESANN Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2015	—	`conf`	Regularized and sparse stochastic k-means for distributed large-scale clustering. IEEE BigData Vilen Jumutc, Rocco Langone, Johan A. K. Suykens
2015	J	`jnl`	Sequential minimal optimization for SVM with pinball loss. Neurocomputing Xiaolin Huang, Lei Shi, Johan A. K. Suykens
2015	J	`jnl`	Signal recovery for jointly sparse vectors with different sensing matrices. Signal Process. Li Li, Xiaolin Huang, Johan A. K. Suykens
2015	J	`jnl`	Two-level ℓ Signal Process. Xiaolin Huang, Yipeng Liu, Lei Shi, Sabine Van Huffel, Johan A. K. Suykens
2015	J	`jnl`	Very Sparse LSSVM Reductions for Large-Scale Data. IEEE Trans. Neural Networks Learn. Syst. Raghvendra Mall, Johan A. K. Suykens
2014	J	`jnl`	A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models. CoRR Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2014	B	`conf`	Agglomerative hierarchical kernel spectral clustering for large scale networks. ESANN Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2014	C	`conf`	Agglomerative hierarchical kernel spectral data clustering. CIDM Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2014	C	`conf`	Alarm prediction in industrial machines using autoregressive LS-SVM models. CIDM Rocco Langone, Carlos Alzate, Abdellatif Bey-Temsamani, Johan A. K. Suykens
2014	J	`jnl`	Asymmetric least squares support vector machine classifiers. Comput. Stat. Data Anal. Xiaolin Huang, Lei Shi, Johan A. K. Suykens
2014	J	`jnl`	Asymmetric v-tube support vector regression. Comput. Stat. Data Anal. Xiaolin Huang, Lei Shi, Kristiaan Pelckmans, Johan A. K. Suykens
2014	C	`conf`	Clustering data over time using kernel spectral clustering with memory. CIDM Rocco Langone, Raghvendra Mall, Johan A. K. Suykens
2014	J	`jnl`	Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning. CoRR Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens
2014	J	`jnl`	EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines. CoRR Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2014	J	`jnl`	EnsembleSVM: a library for ensemble learning using support vector machines. J. Mach. Learn. Res. Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2014	J	`jnl`	Fast Prediction with SVM Models Containing RBF Kernels. CoRR Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2014	—	`conf`	Gene interaction networks boost genetic algorithm performance in biomarker discovery. MCDM Charalampos N. Moschopoulos, Dusan Popovic, Rocco Langone, Johan A. K. Suykens, Bart De Moor, Yves Moreau
2014	—	`conf`	High level high performance computing for multitask learning of time-varying models. CIBD Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens
2014	J	`jnl`	Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization. CoRR Andreas Argyriou, Marco Signoretto, Johan A. K. Suykens
2014	J	`jnl`	Hybrid Coupled Local Minimizers. IEEE Trans. Circuits Syst. I Regul. Pap. Xuyang Lou, Johan A. K. Suykens
2014	J	`jnl`	Improved Initialization for Nonlinear State-Space Modeling. IEEE Trans. Instrum. Meas. Anna Marconato, Jonas Sjöberg, Johan A. K. Suykens, Johan Schoukens
2014	J	`jnl`	Incremental kernel spectral clustering for online learning of non-stationary data. Neurocomputing Rocco Langone, Oscar Mauricio Agudelo, Bart De Moor, Johan A. K. Suykens
2014	B	`conf`	Large scale semi-supervised learning using KSC based model. IJCNN Siamak Mehrkanoon, Johan A. K. Suykens
2014	J	`jnl`	Learning with tensors: a framework based on convex optimization and spectral regularization. Mach. Learn. Marco Signoretto, Dinh Quoc Tran, Lieven De Lathauwer, Johan A. K. Suykens
2014	J	`jnl`	Multi-Class Supervised Novelty Detection. IEEE Trans. Pattern Anal. Mach. Intell. Vilen Jumutc, Johan A. K. Suykens
2014	J	`jnl`	Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks. CoRR Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2014	C	`conf`	New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering. CIDM Vilen Jumutc, Johan A. K. Suykens
2014	J	`jnl`	Non-parallel support vector classifiers with different loss functions. Neurocomputing Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
2014	B	`conf`	Optimal Data Projection for Kernel Spectral Clustering. ESANN Diego Hernán Peluffo-Ordóñez, Carlos Alzate, Johan A. K. Suykens, Germán Castellanos-Domínguez
2014	B	`conf`	Optimal reduced sets for sparse kernel spectral clustering. IJCNN Raghvendra Mall, Siamak Mehrkanoon, Rocco Langone, Johan A. K. Suykens
2014	J	`jnl`	Parameter estimation of delay differential equations: An integration-free LS-SVM approach. Commun. Nonlinear Sci. Numer. Simul. Siamak Mehrkanoon, Saeid Mehrkanoon, Johan A. K. Suykens
2014	J	`jnl`	Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinform. Minta Thomas, Kris De Brabanter, Johan A. K. Suykens, Bart De Moor
2014	J	`jnl`	QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset. Inf. Sci. Dries Geebelen, Kristof Geebelen, Eddy Truyen, Sam Michiels, Johan A. K. Suykens, Joos Vandewalle, Wouter Joosen
2014	J	`jnl`	Quantile regression with ℓ 1 - regularization and Gaussian kernels. Adv. Comput. Math. Lei Shi, Xiaolin Huang, Zheng Tian, Johan A. K. Suykens
2014	J	`jnl`	Ramp loss linear programming support vector machine. J. Mach. Learn. Res. Xiaolin Huang, Lei Shi, Johan A. K. Suykens
2014	—	`conf`	Representative subsets for big data learning using k-NN graphs. IEEE BigData Raghvendra Mall, Vilen Jumutc, Rocco Langone, Johan A. K. Suykens
2014	C	`conf`	Reweighted l 2-Regularized Dual Averaging Approach for Highly Sparse Stochastic Learning. ISNN Vilen Jumutc, Johan A. K. Suykens
2014	B	`conf`	Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning. ESANN Vilen Jumutc, Johan A. K. Suykens
2014	B	`conf`	SVD truncation schemes for fixed-size kernel models. IJCNN Ricardo Castro-Garcia, Siamak Mehrkanoon, Anna Marconato, Johan Schoukens, Johan A. K. Suykens
2014	J	`jnl`	Support Vector Machine Classifier With Pinball Loss. IEEE Trans. Pattern Anal. Mach. Intell. Xiaolin Huang, Lei Shi, Johan A. K. Suykens
2013	—	`conf`	DynOpt: Incorporating dynamics into mean-variance portfolio optimization. CIFEr Marco Signoretto, Johan A. K. Suykens
2013	J	`jnl`	FURS: Fast and Unique Representative Subset selection retaining large-scale community structure. Soc. Netw. Anal. Min. Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2013	B	`conf`	Fixed-size Pegasos for hinge and pinball loss SVM. IJCNN Vilen Jumutc, Xiaolin Huang, Johan A. K. Suykens
2013	—	`conf`	Highly Sparse Reductions to Kernel Spectral Clustering. PReMI Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2013	J	`jnl`	Hinging Hyperplanes for Time-Series Segmentation. IEEE Trans. Neural Networks Learn. Syst. Xiaolin Huang, Marin Matijas, Johan A. K. Suykens
2013	J	`jnl`	Kernel Spectral Clustering for Big Data Networks. Entropy Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2013	B	`conf`	Kernel spectral clustering for dynamic data using multiple kernel learning. IJCNN Diego Hernán Peluffo-Ordóñez, Sergio García-Vega, Rocco Langone, Johan A. K. Suykens, Germán Castellanos-Domínguez
2013	C	`conf`	Kernel spectral clustering for predicting maintenance of industrial machines. CIDM Rocco Langone, Carlos Alzate, Bart De Ketelaere, Johan A. K. Suykens
2013	J	`jnl`	Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties. CoRR Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens
2013	J	`jnl`	Load forecasting using a multivariate meta-learning system. Expert Syst. Appl. Marin Matijas, Johan A. K. Suykens, Slavko Krajcar
2013	B	`conf`	Non-parallel semi-supervised classification based on kernel spectral clustering. IJCNN Siamak Mehrkanoon, Johan A. K. Suykens
2013	Misc	`conf`	Primal-Dual Framework for Feature Selection using Least Squares Support Vector Machines. COMAD Raghvendra Mall, Johan A. K. Suykens, Mohammed El Anbari, Halima Bensmail
2013	J	`jnl`	Risk group detection and survival function estimation for interval coded survival methods. Neurocomputing Vanya Van Belle, Patrick Neven, Vernon Harvey, Sabine Van Huffel, Johan A. K. Suykens, Stephen P. Boyd
2013	—	`conf`	Self-tuned kernel spectral clustering for large scale networks. IEEE BigData Raghvendra Mall, Rocco Langone, Johan A. K. Suykens
2013	—	`conf`	Sleep apnea classification using least-squares support vector machines on single lead ECG. EMBC Carolina Varon, Dries Testelmans, Bertien Buyse, Johan A. K. Suykens, Sabine Van Huffel
2013	B	`conf`	Soft kernel spectral clustering. IJCNN Rocco Langone, Raghvendra Mall, Johan A. K. Suykens
2013	—	`conf`	Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data. PAKDD (1) Raghvendra Mall, Johan A. K. Suykens
2013	J	`jnl`	Stability of Coupled Local Minimizers Within the Lagrange Programming Network Framework. IEEE Trans. Circuits Syst. I Regul. Pap. Xuyang Lou, Johan A. K. Suykens
2013	C	`conf`	Supervised Novelty Detection. CIDM Vilen Jumutc, Johan A. K. Suykens
2013	J	`jnl`	Support vector machines with piecewise linear feature mapping. Neurocomputing Xiaolin Huang, Siamak Mehrkanoon, Johan A. K. Suykens
2013	A*	`conf`	The skweezee system: enabling the design and the programming of squeeze interactions. UIST Karen Vanderloock, Vero Vanden Abeele, Johan A. K. Suykens, Luc Geurts
2013	—	`conf`	Weighted Coordinate-Wise Pegasos. PReMI Vilen Jumutc, Johan A. K. Suykens
2012	J	`jnl`	A mixed effects least squares support vector machine model for classification of longitudinal data. Comput. Stat. Data Anal. Jan Luts, Geert Molenberghs, Geert Verbeke, Sabine Van Huffel, Johan A. K. Suykens
2012	B	`conf`	A semi-supervised formulation to binary kernel spectral clustering. IJCNN Carlos Alzate, Johan A. K. Suykens
2012	J	`jnl`	Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration. IEEE Trans. Biomed. Eng. Devy Widjaja, Carolina Varon, Alexander Dorado, Johan A. K. Suykens, Sabine Van Huffel
2012	J	`jnl`	Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines. IEEE Trans. Neural Networks Learn. Syst. Siamak Mehrkanoon, Tillmann Falck, Johan A. K. Suykens
2012	J	`jnl`	Classification of Multichannel Signals With Cumulant-Based Kernels. IEEE Trans. Signal Process. Marco Signoretto, Emanuele Olivetti, Lieven De Lathauwer, Johan A. K. Suykens
2012	J	`jnl`	Confidence bands for least squares support vector machine classifiers: A regression approach. Pattern Recognit. Kris De Brabanter, Peter Karsmakers, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2012	J	`jnl`	Hierarchical kernel spectral clustering. Neural Networks Carlos Alzate, Johan A. K. Suykens
2012	B	`conf`	Interval coded scoring systems for survival analysis. ESANN Vanya Van Belle, Sabine Van Huffel, Johan A. K. Suykens, Stephen P. Boyd
2012	B	`conf`	Joint Regression and Linear Combination of Time Series for Optimal Prediction. ESANN Dries Geebelen, Kim Batselier, Philippe Dreesen, Marco Signoretto, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
2012	B	`conf`	Kernel spectral clustering for community detection in complex networks. IJCNN Rocco Langone, Carlos Alzate, Johan A. K. Suykens
2012	J	`jnl`	LS-SVM approximate solution to linear time varying descriptor systems. Autom. Siamak Mehrkanoon, Johan A. K. Suykens
2012	J	`jnl`	Optimized Data Fusion for Kernel k-Means Clustering. IEEE Trans. Pattern Anal. Mach. Intell. Shi Yu, Léon-Charles Tranchevent, Xinhai Liu, Wolfgang Glänzel, Johan A. K. Suykens, Bart De Moor, Yves Moreau
2012	J	`jnl`	Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation. IEEE Trans. Neural Networks Learn. Syst. Dries Geebelen, Johan A. K. Suykens, Joos Vandewalle
2012	—	`conf`	Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis. EMBC Carolina Varon, Dries Testelmans, Bertien Buyse, Johan A. K. Suykens, Sabine Van Huffel
2012	B	`conf`	Robustness of kernel based regression: Influence and weight functions. IJCNN Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2012	J	`jnl`	Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface. Neurocomputing Adrien Combaz, Nikolay Chumerin, Nikolay V. Manyakov, Arne Robben, Johan A. K. Suykens, Marc M. Van Hulle
2011	J	`jnl`	A kernel-based framework to tensorial data analysis. Neural Networks Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens
2011	J	`jnl`	Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. IEEE Trans. Neural Networks Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2011	—	`conf`	Automatic Seizure Detection Incorporating Structural Information. ICANN (1) Borbála Hunyadi, Maarten De Vos, Marco Signoretto, Johan A. K. Suykens, Wim Van Paesschen, Sabine Van Huffel
2011	J	`jnl`	First and Second Order SMO Algorithms for LS-SVM Classifiers. Neural Process. Lett. Jorge López Lázaro, Johan A. K. Suykens
2011	J	`jnl`	Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinform. Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens
2011	J	`jnl`	Kernel Regression in the Presence of Correlated Errors. J. Mach. Learn. Res. Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2011	J	`jnl`	Learning Transformation Models for Ranking and Survival Analysis. J. Mach. Learn. Res. Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
2011	B	`conf`	Modularity-based model selection for kernel spectral clustering. IJCNN Rocco Langone, Carlos Alzate, Johan A. K. Suykens
2011	J	`jnl`	On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation. Comput. Biol. Medicine Geert J. Postma, Jan Luts, Albert J. Idema, Margarida Julià-Sapé, Àngel Moreno-Torres, Witek Gajewicz, Johan A. K. Suykens, Arend Heerschap, Sabine Van Huffel, Lutgarde M. C. Buydens
2011	J	`jnl`	Optimized data fusion for K-means Laplacian clustering. Bioinform. Shi Yu, Xinhai Liu, Léon-Charles Tranchevent, Wolfgang Glänzel, Johan A. K. Suykens, Bart De Moor, Yves Moreau
2011	B	`conf`	Out-of-sample eigenvectors in kernel spectral clustering. IJCNN Carlos Alzate, Johan A. K. Suykens
2011	B	`conf`	Sparse LS-SVMs with L0 - norm minimization. ESANN Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens
2011	J	`jnl`	Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach. Learn. Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens
2011	J	`jnl`	Sparse kernel spectral clustering models for large-scale data analysis. Neurocomputing Carlos Alzate, Johan A. K. Suykens
2011	J	`jnl`	Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif. Intell. Medicine Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens
2011	B	`conf`	Symbolic computing of LS-SVM based models. ESANN Siamak Mehrkanoon, Li Jiang, Carlos Alzate, Johan A. K. Suykens
2011	J	`jnl`	Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data. IEEE Signal Process. Lett. Marco Signoretto, Raf Van de Plas, Bart De Moor, Johan A. K. Suykens
2010	J	`jnl`	Coupled Simulated Annealing. IEEE Trans. Syst. Man Cybern. Part B Samuel Xavier de Souza, Johan A. K. Suykens, Joos Vandewalle, Désiré Bollé
2010	—	`conf`	Efficient adaptive filtering for smooth linear FIR models. EUSIPCO Kristiaan Pelckmans, Toon van Waterschoot, Johan A. K. Suykens
2010	—	`conf`	Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization. CDC Ion Necoara, Ioan Dumitrache, Johan A. K. Suykens
2010	B	`conf`	Highly sparse kernel spectral clustering with predictive out-of-sample extensions. ESANN Carlos Alzate, Johan A. K. Suykens
2010	J	`jnl`	Improved dual decomposition based optimization for DSL dynamic spectrum management. IEEE Trans. Signal Process. Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen
2010	—	`conf`	Improved non-parametric sparse recovery with data matched penalties. CIP Marco Signoretto, Kristiaan Pelckmans, Lieven De Lathauwer, Johan A. K. Suykens
2010	—	`conf`	Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. ICANN (2) Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens
2010	J	`jnl`	L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinform. Shi Yu, Tillmann Falck, Anneleen Daemen, Léon-Charles Tranchevent, Johan A. K. Suykens, Bart De Moor, Yves Moreau
2010	—	`conf`	Linear parametric noise models for Least Squares Support Vector Machines. CDC Tillmann Falck, Johan A. K. Suykens, Bart De Moor
2010	J	`jnl`	Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA. IEEE Trans. Pattern Anal. Mach. Intell. Carlos Alzate, Johan A. K. Suykens
2010	—	`conf`	Nuclear norm regularization for overparametrized Hammerstein systems. CDC Tillmann Falck, Johan A. K. Suykens, Johan Schoukens, Bart De Moor
2010	B	`conf`	On the use of a clinical kernel in survival analysis. ESANN Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
2010	J	`jnl`	Optimized fixed-size kernel models for large data sets. Comput. Stat. Data Anal. Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2010	B	`conf`	Polynomial componentwise LS-SVM: Fast variable selection using low rank updates. IJCNN Fabian Ojeda, Tillmann Falck, Bart De Moor, Johan A. K. Suykens
2010	J	`jnl`	Robustness of reweighted Least Squares Kernel Based Regression. J. Multivar. Anal. Michiel Debruyne, Andreas Christmann, Mia Hubert, Johan A. K. Suykens
2010	—	`conf`	Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. PRIB Fabian Ojeda, Marco Signoretto, Raf Van de Plas, Etienne Waelkens, Bart De Moor, Johan A. K. Suykens
2009	—	`conf`	A dual interior-point distributed algorithm for large-scale data networks optimization. ECC Ion Necoara, Johan A. K. Suykens
2009	B	`conf`	A regularized formulation for spectral clustering with pairwise constraints. IJCNN Carlos Alzate, Johan A. K. Suykens
2009	—	`conf`	An improved dual decomposition approach to DSL dynamic spectrum management. EUSIPCO Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen
2009	B	`conf`	Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. CBMS Jan Luts, Johan A. K. Suykens, Sabine Van Huffel, Teresa Laudadio, Sofie Van Cauter, Uwe Himmelreich, Enrique Molla, Jose Piquer, M. Carmen Martínez-Bisbal, Bernardo Celda
2009	—	`conf`	Distributed nonlinear optimal control using sequential convex programming and smoothing techniques. CDC Ion Necoara, Carlo Savorgnan, Dinh Quoc Tran, Johan A. K. Suykens, Moritz Diehl
2009	C	`conf`	Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling. ICMLA Adrien Combaz, Nikolay V. Manyakov, Nikolay Chumerin, Johan A. K. Suykens, Marc M. Van Hulle
2009	—	`conf`	Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints. IWANN (1) Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
2009	—	`conf`	Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering. ICANN (2) Carlos Alzate, Marcelo Espinoza, Bart De Moor, Johan A. K. Suykens
2009	J	`jnl`	Least conservative support and tolerance tubes. IEEE Trans. Inf. Theory Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2009	—	`conf`	MINLIP: Efficient Learning of Transformation Models. ICANN (1) Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
2009	Misc	`conf`	P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface. KI Nikolay Chumerin, Nikolay V. Manyakov, Adrien Combaz, Johan A. K. Suykens, Refet Firat Yazicioglu, Tom Torfs, Patrick Merken, Herc P. Neves, Chris Van Hoof, Marc M. Van Hulle
2009	—	`conf`	Robustness analysis for Least Squares kernel based regression: an optimization approach. CDC Tillmann Falck, Johan A. K. Suykens, Bart De Moor
2009	—	`conf`	Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. ICANN (1) Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens, Mia Hubert, Bart De Moor
2009	B	`conf`	Transductively Learning from Positive Examples Only. ESANN Kristiaan Pelckmans, Johan A. K. Suykens
2008	—	`conf`	A proximal center-based decomposition method for multi-agent convex optimization. CDC Ion Necoara, Johan A. K. Suykens
2008	J	`jnl`	A regularized kernel CCA contrast function for ICA. Neural Networks Carlos Alzate, Johan A. K. Suykens
2008	—	`conf`	An Online Algorithm for Learning a Labeling of a Graph. MLG@ICML Kristiaan Pelckmans, Johan A. K. Suykens
2008	J	`jnl`	Application of a Smoothing Technique to Decomposition in Convex Optimization. IEEE Trans. Autom. Control. Ion Necoara, Johan A. K. Suykens
2008	—	`conf`	Application of the proximal center decomposition method to distributed model predictive control. CDC Ion Necoara, Minh Dang Doan, Johan A. K. Suykens
2008	J	`jnl`	Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point. IEEE Trans. Neural Networks Johan A. K. Suykens
2008	J	`jnl`	Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function. IEEE Trans. Neural Networks Carlos Alzate, Johan A. K. Suykens
2008	J	`jnl`	Low rank updated LS-SVM classifiers for fast variable selection. Neural Networks Fabian Ojeda, Johan A. K. Suykens, Bart De Moor
2008	—	`conf`	Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. ICANN (1) Marco Signoretto, Kristiaan Pelckmans, Johan A. K. Suykens
2008	J	`jnl`	Robust triple mode MPC. Int. J. Control Lars Imsland, J. Anthony Rossiter, Bert Pluymers, Johan A. K. Suykens
2008	B	`conf`	Sparse kernel models for spectral clustering using the incomplete Cholesky decomposition. IJCNN Carlos Alzate, Johan A. K. Suykens
2008	B	`conf`	Survival SVM: a practical scalable algorithm. ESANN Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
2007	J	`jnl`	A Convex Approach to Validation-Based Learning of the Regularization Constant. IEEE Trans. Neural Networks Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2007	—	`conf`	A Risk Minimization Principle for a Class of Parzen Estimators. NIPS Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2007	J	`jnl`	A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artif. Intell. Medicine Jan Luts, Arend Heerschap, Johan A. K. Suykens, Sabine Van Huffel
2007	J	`jnl`	Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis. IEEE Trans. Inf. Technol. Biomed. Chuan Lu, Andy Devos, Johan A. K. Suykens, Carles Arús, Sabine Van Huffel
2007	—	`conf`	Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression. ICANN (2) Ben Van Calster, Jan Luts, Johan A. K. Suykens, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel
2007	B	`conf`	Convex optimization for the design of learning machines. ESANN Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2007	J	`jnl`	Efficiently updating and tracking the dominant kernel principal components. Neural Networks Luc Hoegaerts, Lieven De Lathauwer, Ivan Goethals, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2007	A	`conf`	Fixed-size kernel logistic regression for phoneme classification. INTERSPEECH Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens, Hugo Van hamme
2007	B	`conf`	ICA through an LS-SVM based Kernel CCA Measure for Independence. IJCNN Carlos Alzate, Johan A. K. Suykens
2007	A	`conf`	Margin based Transductive Graph Cuts using Linear Programming. AISTATS Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor
2007	B	`conf`	Multi-class kernel logistic regression: a fixed-size implementation. IJCNN Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens
2007	J	`jnl`	Partial Synchronization in oscillator Arrays with Asymmetric Coupling. Int. J. Bifurc. Chaos Dániel Hillier, Serkan Günel, Johan A. K. Suykens, Joos Vandewalle
2007	—	`conf`	State-of-the-Art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition. ICASSP (4) Joos Vandewalle, Johan A. K. Suykens, Bart De Moor, Amaury Lendasse
2007	J	`jnl`	Support and Quantile Tubes CoRR Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2007	—	`conf`	Transductive Rademacher Complexities for Learning Over a Graph. MLG Kristiaan Pelckmans, Johan A. K. Suykens
2007	B	`conf`	Variable selection by rank-one updates for least squares support vector machines. IJCNN Fabian Ojeda, Johan A. K. Suykens, Bart De Moor
2006	B	`conf`	A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods. IJCNN Carlos Alzate, Johan A. K. Suykens
2006	J	`jnl`	A process model to develop an internal rating system: Sovereign credit ratings. Decis. Support Syst. Tony Van Gestel, Bart Baesens, Peter Van Dijcke, Joao Garcia, Johan A. K. Suykens, Jan Vanthienen
2006	J	`jnl`	Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. Mach. Learn. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2006	J	`jnl`	Bayesian kernel based classification for financial distress detection. Eur. J. Oper. Res. Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens
2006	J	`jnl`	Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting. Comput. Manag. Sci. Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2006	J	`jnl`	Learning of spatiotemporal behaviour in cellular neural networks. Int. J. Circuit Theory Appl. Samuel Xavier de Souza, Johan A. K. Suykens, Joos Vandewalle
2006	C	`conf`	Multi-scroll and hypercube attractors from Josephson junctions. ISCAS Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2006	C	`conf`	Robust synthesis of constrained linear state feedback using LMIs and polyhedral invariant sets. ACC Bert Pluymers, Mayuresh V. Kothare, Johan A. K. Suykens, Bart De Moor
2006	C	`conf`	Robust triple mode MPC. ACC Lars Imsland, J. Anthony Rossiter, Bert Pluymers, Johan A. K. Suykens
2006	J	`jnl`	Spatiotemporal Pattern Formation on the ACE16K CNN Chip. Int. J. Bifurc. Chaos Müstak E. Yalçin, Johan A. K. Suykens
2005	J	`jnl`	Building sparse representations and structure determination on LS-SVM substrates. Neurocomputing Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	Componentwise Least Squares Support Vector Machines CoRR Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	Componentwise Support Vector Machines for Structure Detection. ICANN (2) Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	Constrained linear MPC with time-varying terminal cost using convex combinations. Autom. Bert Pluymers, L. Roobrouck, J. Buijs, Johan A. K. Suykens, Bart De Moor
2005	B	`conf`	Extending kernel principal component analysis to general underlying loss functions. IJCNN Carlos Alzate, Johan A. K. Suykens
2005	J	`jnl`	Handling missing values in support vector machine classifiers. Neural Networks Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	Identification of MIMO Hammerstein models using least squares support vector machines. Autom. Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	Imposing Symmetry in Least Squares Support Vector Machines Regression. CDC/ECC Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2005	C	`conf`	Interpolation based MPC for LPV systems using polyhedral invariant sets. ACC Bert Pluymers, John Anthony Rossiter, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	Interpolation based robust MPC with exact constraint handling. CDC/ECC J. Anthony Rossiter, Yihang Ding, Bert Pluymers, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	Kernel based partially linear models and nonlinear identification. IEEE Trans. Autom. Control. Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2005	Misc	`conf`	Load Forecasting Using Fixed-Size Least Squares Support Vector Machines. IWANN Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	M@CBETH: Optimizing Clinical Microarray Classification. CSB Workshops Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Ignace Vergote, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	M@CBETH: a microarray classification benchmarking tool. Bioinform. Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Johan A. K. Suykens, Bart De Moor
2005	B	`conf`	Maximal variation and missing values for componentwise support vector machines. IJCNN Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor, Jos De Brabanter
2005	J	`jnl`	Min-max feedback MPC using a time-varying terminal constraint set and comments on "Efficient robust constrained model predictive control with a time-varying terminal constraint set". Syst. Control. Lett. Bert Pluymers, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	On Model Complexity Control in Identification of Hammerstein Systems. CDC/ECC Kristiaan Pelckmans, Johan A. K. Suykens, Ivan Goethals, Bart De Moor
2005	J	`jnl`	Primal-Dual Monotone Kernel Regression. Neural Process. Lett. Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	Spatiotemporal pattern formation in the ACE16k CNN chip. ISCAS (6) Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2005	J	`jnl`	Subset based least squares subspace regression in RKHS. Neurocomputing Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2005	J	`jnl`	Subspace identification of Hammerstein systems using least squares support vector machines. IEEE Trans. Autom. Control. Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2005	—	`conf`	Subspace intersection identification of Hammerstein-Wiener systems. CDC/ECC Ivan Goethals, Kristiaan Pelckmans, Luc Hoegaerts, Johan A. K. Suykens, Bart De Moor
2005	J	`jnl`	The differogram: Non-parametric noise variance estimation and its use for model selection. Neurocomputing Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2005	C	`conf`	The efficient computation of polyhedral invariant sets for linear systems with polytopic uncertainty. ACC Bert Pluymers, John Anthony Rossiter, Johan A. K. Suykens, Bart De Moor
2004	B	`conf`	A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines. ICONIP Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2004	—	`conf`	A double scroll based true random bit generator. ISCAS (4) Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2004	J	`jnl`	Benchmarking Least Squares Support Vector Machine Classifiers. Mach. Learn. Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle
2004	J	`jnl`	Brain tumor classification based on long echo proton MRS signals. Artif. Intell. Medicine Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, Franklyn A. Howe, Carles Majós, Àngel Moreno-Torres, M. Van Der Graaf, Anne Rosemary Tate, Carles Arús, Sabine Van Huffel
2004	—	`conf`	Learning from General Label Constraints. SSPR/SPR Tijl De Bie, Johan A. K. Suykens, Bart De Moor
2004	—	`conf`	Linear MPC with time-varying terminal cost using sparse convex combinations and bisection search. CDC Bert Pluymers, Johan A. K. Suykens, Bart De Moor
2004	B	`conf`	Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. ICONIP Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2004	—	`conf`	Partially linear models and least squares support vector machines. CDC Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2004	B	`conf`	Primal space sparse kernel partial least squares regression for large scale problems. IJCNN Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2004	B	`conf`	Regularization constants in LS-SVMs: a fast estimate via convex optimization. IJCNN Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2004	—	`conf`	Robust finite-horizon MPC using optimal worst-case closed-loop predictions. CDC Bert Pluymers, Johan A. K. Suykens, Bart De Moor
2004	B	`conf`	Sparse LS-SVMs using additive regularization with a penalized validation criterion. ESANN Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
2004	J	`jnl`	Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinform. Nathalie Pochet, Frank De Smet, Johan A. K. Suykens, Bart De Moor
2004	J	`jnl`	Toward CNN chip-specific robustness. IEEE Trans. Circuits Syst. I Regul. Pap. Samuel Xavier de Souza, Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2004	J	`jnl`	True random bit generation from a double-scroll attractor. IEEE Trans. Circuits Syst. I Regul. Pap. Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2003	J	`jnl`	A support vector machine formulation to PCA analysis and its kernel version. IEEE Trans. Neural Networks Johan A. K. Suykens, Tony Van Gestel, Joos Vandewalle, Bart De Moor
2003	—	`conf`	Bankruptcy prediction with least squares support vector machine classifiers. CIFEr Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Marcelo Espinoza, Dirk-Emma Baestaens, Jan Vanthienen, Bart De Moor
2003	J	`jnl`	Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. Bart Baesens, Tony Van Gestel, Stijn Viaene, M. Stepanova, Johan A. K. Suykens, Jan Vanthienen
2003	B	`conf`	Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. AIME Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Dirk Timmerman, Ignace Vergote
2003	—	`conf`	Coupled chaotic simulated annealing processes. ISCAS (3) Johan A. K. Suykens, Müstak E. Yalçin, Joos Vandewalle
2003	J	`jnl`	Identification of positive real models in subspace identification by using regularization. IEEE Trans. Autom. Control. Ivan Goethals, Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor
2003	B	`conf`	Kernel PLS variants for regression. ESANN Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2003	—	`conf`	Least squares support vector machines and primal space estimation. CDC Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
2003	J	`jnl`	Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. Artif. Intell. Medicine Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Ignace Vergote, Dirk Timmerman
2003	—	`conf`	Variogram based noise variance estimation and its use in kernel based regression. NNSP Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
2002	J	`jnl`	Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. Neural Comput. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
2002	C	`conf`	Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models. ICANN Bart Hamers, Johan A. K. Suykens, Bart De Moor
2002	J	`jnl`	Families of scroll Grid attractors. Int. J. Bifurc. Chaos Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle, Serdar Özoguz
2002	J	`jnl`	Intelligence and Cooperative Search by Coupled Local Minimizers CoRR Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2002	—	`book`	Least Squares Support Vector Machines Johan A. K. Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle
2002	J	`jnl`	Multiclass LS SVMs Moderated Outputs and Coding Decoding Schemes. Neural Process. Lett. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
2002	B	`conf`	Prediction of mental development of preterm newborns at birth time using LS-SVM. ESANN Lieveke Ameye, Chuan Lu, Lukas Lukas, Jos De Brabanter, Johan A. K. Suykens, Sabine Van Huffel, Hans Daniels, Gunnar Naulaers, Hugo Devlieger
2002	C	`conf`	Robust Cross-Validation Score Function for Non-linear Function Estimation. ICANN Jos De Brabanter, Kristiaan Pelckmans, Johan A. K. Suykens, Joos Vandewalle
2002	B	`conf`	The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals. ESANN Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, Sabine Van Huffel, Anne Rosemary Tate, Carles Majós, Carles Arús
2002	J	`jnl`	Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing Johan A. K. Suykens, Jos De Brabanter, Lukas Lukas, Joos Vandewalle
2001	B	`conf`	Automatic relevance determination for Least Squares Support Vector Machines classifiers. ESANN Tony Van Gestel, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
2001	J	`jnl`	Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans. Neural Networks Tony Van Gestel, Johan A. K. Suykens, Dirk-Emma Baestaens, Annemie Lambrechts, Gert R. G. Lanckriet, Bruno Vandaele, Bart De Moor, Joos Vandewalle
2001	J	`jnl`	Identification of stable models in subspace identification by using regularization. IEEE Trans. Autom. Control. Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor
2001	J	`jnl`	Improved Long-Term Temperature Prediction by Chaining of Neural Networks. Int. J. Neural Syst. Michel Duhoux, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
2001	J	`jnl`	Intelligence and Cooperative Search by Coupled Local Minimizers. Int. J. Bifurc. Chaos Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2001	C	`conf`	Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. ICANN Tony Van Gestel, Johan A. K. Suykens, Jos De Brabanter, Bart De Moor, Joos Vandewalle
2001	J	`jnl`	Knowledge discovery in a direct marketing case using least squares support vector machines. Int. J. Intell. Syst. Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
2001	J	`jnl`	Master-Slave Synchronization of Lur'e Systems with Time-Delay. Int. J. Bifurc. Chaos Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
2001	J	`jnl`	Optimal control by least squares support vector machines. Neural Networks Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
2001	J	`jnl`	Support Vector Machines: A Nonlinear Modelling and Control Perspective. Eur. J. Control Johan A. K. Suykens
2000	B	`conf`	An empirical assessment of kernel type performance for least squares support vector machine classifiers. KES Bart Baesens, Stijn Viaene, Tony Van Gestel, Johan A. K. Suykens, Guido Dedene, Bart De Moor, Jan Vanthienen
2000	J	`jnl`	Chaos Synchronization: a Lagrange Programming Network Approach. Int. J. Bifurc. Chaos Johan A. K. Suykens, Joos Vandewalle
2000	—	`conf`	Imposing stability in subspace identification by regularization. CDC Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor
2000	—	`conf`	Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. PKDD Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
2000	J	`jnl`	Robust local stability of multilayer recurrent neural networks. IEEE Trans. Neural Networks Learn. Syst. Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
2000	C	`conf`	Sparse approximation using least squares support vector machines. ISCAS Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle
2000	B	`conf`	Sparse least squares Support Vector Machine classifiers. ESANN Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle
2000	B	`conf`	The K.U.Leuven competition data: a challenge for advanced neural network techniques. ESANN Johan A. K. Suykens, Joos Vandewalle
1999	J	`jnl`	Chaos control using least‐squares support vector machines. Int. J. Circuit Theory Appl. Johan A. K. Suykens, Joos Vandewalle
1999	B	`conf`	Continuous time NLq theory: absolute stability criteria. IJCNN Johan A. K. Suykens, Joos Vandewalle
1999	J	`jnl`	Least Squares Support Vector Machine Classifiers. Neural Process. Lett. Johan A. K. Suykens, Joos Vandewalle
1999	J	`jnl`	Lur'e systems with multilayer perceptron and recurrent neural networks: absolute stability and dissipativity. IEEE Trans. Autom. Control. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
1999	B	`conf`	Multiclass least squares support vector machines. IJCNN Johan A. K. Suykens, Joos Vandewalle
1999	—	`conf`	On the realization of n-scroll attractors. ISCAS (5) Müstak E. Yalçin, Johan A. K. Suykens, Joos Vandewalle
1999	J	`jnl`	Training multilayer perceptron classifiers based on a modified support vector method. IEEE Trans. Neural Networks Johan A. K. Suykens, Joos Vandewalle
1998	J	`jnl`	Application of NL Eur. J. Control Herman Verrelst, Kristel Van Acker, Johan A. K. Suykens, Bart Motmans, Bart De Moor, Joos Vandewalle
1998	B	`conf`	Improved generalization ability of neurocontrollers by imposing NLq stability constraints. ESANN Johan A. K. Suykens, Joos Vandewalle
1998	J	`jnl`	On-Line Learning Fokker-Planck Machine. Neural Process. Lett. Johan A. K. Suykens, Herman Verrelst, Joos Vandewalle
1997	J	`jnl`	NL Neural Networks Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
1997	J	`jnl`	NL IEEE Trans. Signal Process. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
1997	—	`conf`	Robust NL ICNN Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
1996	—	`book`	Artificial neural networks for modelling and control of non-linear systems. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
1996	J	`jnl`	Discrete Time Interconnected Cellular Neural Networks Within NLq Theory. Int. J. Circuit Theory Appl. Johan A. K. Suykens, Joos Vandewalle
1996	J	`jnl`	Modelling the Belgian Gas Consumption Using Neural Networks. Neural Process. Lett. Johan A. K. Suykens, Philippe Lemmerling, Wouter Favoreel, Bart De Moor, M. Crepel, P. Briol
1995	C	`conf`	Generalized Cellular Neural Networks Represented in he NL ISCAS Johan A. K. Suykens, Joos Vandewalle
1995	—	`conf`	Global asymptotic stability criteria for multilayer recurrent neural networks with applications to modelling and control. ICNN Johan A. K. Suykens, Joos Vandewalle
1995	B	`conf`	NLq theory: unifications in the theory of neural networks, systems and control. ESANN Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
1995	—	`conf`	On the identification of a chaotic system by means of recurrent neural state space models. ICNN Johan A. K. Suykens, Joos Vandewalle
1994	J	`jnl`	Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points. Neural Networks Johan A. K. Suykens, Bart De Moor, Joos Vandewalle