Marco Loog - RankMe

264 papers A* 7A 7B 35C 1Journal 120Unranked 82

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Generalization performance distributions along learning curves. Pattern Recognit. Lett. O. Taylan Turan, Marco Loog, David M. J. Tax
2026	B	`conf`	On Sample-Wise Strict Monotonicity with a Gradient Update. IDA O. Taylan Turan, Marco Loog, David M. J. Tax
2025	J	`jnl`	Abstraction for Bayesian Reinforcement Learning in Factored POMDPs. Trans. Mach. Learn. Res. Rolf A. N. Starre, Sammie Katt, Mustafa Mert Çelikok, Marco Loog, Frans A. Oliehoek
2025	B	`conf`	Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data. IDA Marco Loog, Jesse H. Krijthe, Manuele Bicego
2025	J	`jnl`	Learning Learning Curves. Pattern Anal. Appl. O. Taylan Turan, David M. J. Tax, Tom J. Viering, Marco Loog
2025	—	`conf`	The Vanishing Empirical Variance in Randomly Initialized Deep ReLU Networks. ECML/PKDD (4) Michal Grzejdziak-Zdziarski, David M. J. Tax, Marco Loog
2024	J	`jnl`	Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures. CoRR Yuko Kato, David M. J. Tax, Marco Loog
2023	—	`conf`	A Review of Nonconformity Measures for Conformal Prediction in Regression. COPA Yuko Kato, David M. J. Tax, Marco Loog
2023	J	`jnl`	Also for k-means: more data does not imply better performance. Mach. Learn. Marco Loog, Jesse H. Krijthe, Manuele Bicego
2023	J	`jnl`	An Analysis of Model-Based Reinforcement Learning From Abstracted Observations. Trans. Mach. Learn. Res. Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek
2023	J	`jnl`	Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. IEEE Trans. Pattern Anal. Mach. Intell. Alexander Mey, Marco Loog
2023	B	`conf`	Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response. RECOMB Soufiane Mourragui, Marco Loog, Mirrelijn M. van Nee, Mark A. van de Wiel, Marcel J. T. Reinders, Lodewyk F. A. Wessels
2023	J	`jnl`	The Shape of Learning Curves: A Review. IEEE Trans. Pattern Anal. Mach. Intell. Tom J. Viering, Marco Loog
2023	A*	`conf`	Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models. NeurIPS Chirag Raman, Alec Nonnemaker, Amelia Villegas-Morcillo, Hayley Hung, Marco Loog
2022	J	`jnl`	A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. CoRR Marco Loog, Tom J. Viering
2022	—	`conf`	A View on Model Misspecification in Uncertainty Quantification. BNAIC/BENELEARN Yuko Kato, David M. J. Tax, Marco Loog
2022	J	`jnl`	A view on model misspecification in uncertainty quantification. CoRR Yuko Kato, David M. J. Tax, Marco Loog
2022	J	`jnl`	An Analysis of Abstracted Model-Based Reinforcement Learning. CoRR Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
2022	A*	`conf`	Enhancing Classifier Conservativeness and Robustness by Polynomiality. CVPR Ziqi Wang, Marco Loog
2022	J	`jnl`	Enhancing Classifier Conservativeness and Robustness by Polynomiality. CoRR Ziqi Wang, Marco Loog
2022	—	`conf`	Explaining Two Strange Learning Curves. BNAIC/BENELEARN Zhiyi Chen, Marco Loog, Jesse H. Krijthe
2022	—	`conf`	LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. ECML/PKDD (5) Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn
2022	—	`conf`	Model-Based Reinforcement Learning with State Abstraction: A Survey. BNAIC/BENELEARN Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
2022	J	`jnl`	On the reusability of samples in active learning. CoRR Gijs van Tulder, Marco Loog
2022	—	`conf`	Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues. ECCV Workshops (3) Chirag Raman, Hayley Hung, Marco Loog
2022	J	`jnl`	To Actively Initialize Active Learning. Pattern Recognit. Yazhou Yang, Marco Loog
2022	J	`jnl`	Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts. CoRR Chirag Raman, Hayley Hung, Marco Loog
2021	J	`jnl`	A Review of Domain Adaptation without Target Labels. IEEE Trans. Pattern Anal. Mach. Intell. Wouter M. Kouw, Marco Loog
2021	A*	`conf`	Consistency and Finite Sample Behavior of Binary Class Probability Estimation. AAAI Alexander Mey, Marco Loog
2021	J	`jnl`	Nearest Neighbor-based Importance Weighting. CoRR Marco Loog
2021	—	`conf`	ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. RRPR Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert
2021	J	`jnl`	Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. IEEE Trans. Image Process. Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert
2021	J	`jnl`	Resolution learning in deep convolutional networks using scale-space theory. CoRR Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert
2021	J	`jnl`	Robust domain-adaptive discriminant analysis. Pattern Recognit. Lett. Wouter M. Kouw, Marco Loog
2021	J	`jnl`	Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations. CoRR Chirag Raman, Hayley Hung, Marco Loog
2021	J	`jnl`	The Shape of Learning Curves: a Review. CoRR Tom J. Viering, Marco Loog
2020	J	`jnl`	A Brief Prehistory of Double Descent. CoRR Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax
2020	B	`conf`	A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. IDA Alexander Mey, Tom Julian Viering, Marco Loog
2020	B	`conf`	Bayesian Active Learning for Maximal Information Gain on Model Parameters. ICPR Kasra Arnavaz, Aasa Feragen, Oswin Krause, Marco Loog
2020	A	`conf`	Black Magic in Deep Learning: How Human Skill Impacts Network Training. BMVC Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert
2020	J	`jnl`	Black Magic in Deep Learning: How Human Skill Impacts Network Training. CoRR Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert
2020	B	`conf`	Making Learners (More) Monotone. IDA Tom Julian Viering, Alexander Mey, Marco Loog
2020	J	`jnl`	ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility. CoRR Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert
2020	B	`conf`	Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. ICPR Ziqi Wang, Marco Loog, Jan van Gemert
2020	J	`jnl`	Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. CoRR Ziqi Wang, Marco Loog, Jan van Gemert
2020	A	`conf`	Semi-supervised learning, causality, and the conditional cluster assumption. UAI Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf
2020	—	`conf`	Target Robust Discriminant Analysis. S+SSPR Wouter M. Kouw, Marco Loog
2019	J	`jnl`	A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. CoRR Alexander Mey, Tom J. Viering, Marco Loog
2019	J	`jnl`	A dissimilarity-based multiple instance learning approach for protein remote homology detection. Pattern Recognit. Lett. Antonella Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax
2019	J	`jnl`	A review of single-source unsupervised domain adaptation. CoRR Wouter M. Kouw, Marco Loog
2019	J	`jnl`	Consistency and Finite Sample Behavior of Binary Class Probability Estimation. CoRR Alexander Mey, Marco Loog
2019	J	`jnl`	Gaussian process variance reduction by location selection. Pattern Recognit. Lett. Lorenzo Bottarelli, Marco Loog
2019	J	`jnl`	How to Manipulate CNNs to Make Them Lie: the GradCAM Case. CoRR Tom J. Viering, Ziqi Wang, Marco Loog, Elmar Eisemann
2019	J	`jnl`	Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. CoRR Alexander Mey, Marco Loog
2019	—	`conf`	Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks. ISBI Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik
2019	J	`jnl`	Making Learners (More) Monotone. CoRR Tom J. Viering, Alexander Mey, Marco Loog
2019	A*	`conf`	Minimizers of the Empirical Risk and Risk Monotonicity. NeurIPS Marco Loog, Tom J. Viering, Alexander Mey
2019	J	`jnl`	Minimizers of the Empirical Risk and Risk Monotonicity. CoRR Marco Loog, Tom J. Viering, Alexander Mey
2019	J	`jnl`	Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings. Mach. Vis. Appl. Yuan Zeng, Jan C. A. van der Lubbe, Marco Loog
2019	J	`jnl`	Nuclear discrepancy for single-shot batch active learning. Mach. Learn. Tom J. Viering, Jesse H. Krijthe, Marco Loog
2019	—	`conf`	On the Statistical Detection of Adversarial Instances over Encrypted Data. STM Mina Sheikhalishahi, Majid Nateghizad, Fabio Martinelli, Zekeriya Erkin, Marco Loog
2019	A*	`conf`	Open Problem: Monotonicity of Learning. COLT Tom J. Viering, Alexander Mey, Marco Loog
2019	J	`jnl`	PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinform. Soufiane Mourragui, Marco Loog, Mark A. van de Wiel, Marcel J. T. Reinders, Lodewyk F. A. Wessels
2019	—	`conf`	Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP Wouter M. Kouw, Jesse H. Krijthe, Marco Loog
2019	A	`conf`	Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS Julius von Kügelgen, Alexander Mey, Marco Loog
2019	J	`jnl`	Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf
2019	J	`jnl`	Single shot active learning using pseudo annotators. Pattern Recognit. Yazhou Yang, Marco Loog
2018	J	`jnl`	A benchmark and comparison of active learning for logistic regression. Pattern Recognit. Yazhou Yang, Marco Loog
2018	J	`jnl`	A variance maximization criterion for active learning. Pattern Recognit. Yazhou Yang, Marco Loog
2018	B	`conf`	Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI. Image Processing Marijn van Stralen, Y. Zhou, P. J. Wozny, Peter R. Seevinck, Marco Loog
2018	J	`jnl`	Distance Based Source Domain Selection for Sentiment Classification. CoRR Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani
2018	B	`conf`	Effects of sampling skewness of the importance-weighted risk estimator on model selection. ICPR Wouter M. Kouw, Marco Loog
2018	J	`jnl`	Effects of sampling skewness of the importance-weighted risk estimator on model selection. CoRR Wouter M. Kouw, Marco Loog
2018	—	`conf`	Gradient Descent for Gaussian Processes Variance Reduction. S+SSPR Lorenzo Bottarelli, Marco Loog
2018	J	`jnl`	Learning an MR acquisition-invariant representation using Siamese neural networks. CoRR Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik
2018	—	`conf`	Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. S+SSPR Antonella Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax
2018	J	`jnl`	Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR Julius von Kügelgen, Alexander Mey, Marco Loog
2018	J	`jnl`	Single Shot Active Learning using Pseudo Annotators. CoRR Yazhou Yang, Marco Loog
2018	J	`jnl`	Target Contrastive Pessimistic Discriminant Analysis. CoRR Wouter M. Kouw, Marco Loog
2018	J	`jnl`	Template Matching via Densities on the Roto-Translation Group. IEEE Trans. Pattern Anal. Mach. Intell. Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits
2018	A*	`conf`	The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	A Variance Maximization Criterion for Active Learning. CoRR Yazhou Yang, Marco Loog
2017	J	`jnl`	Active Learning Using Uncertainty Information. CoRR Yazhou Yang, Marco Loog
2017	J	`jnl`	Classification of COPD with Multiple Instance Learning. CoRR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne
2017	J	`jnl`	Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision. Pattern Recognit. Jianxin Wu, Xiang Bai, Marco Loog, Fabio Roli, Zhi-Hua Zhou
2017	J	`jnl`	Label Stability in Multiple Instance Learning. CoRR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
2017	J	`jnl`	MR Acquisition-Invariant Representation Learning. CoRR Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik
2017	J	`jnl`	Nuclear Discrepancy for Active Learning. CoRR Tom J. Viering, Jesse H. Krijthe, Marco Loog
2017	A	`conf`	Object-Extent Pooling for Weakly Supervised Single-Shot Localization. BMVC Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert
2017	J	`jnl`	Object-Extent Pooling for Weakly Supervised Single-Shot Localization. CoRR Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert
2017	J	`jnl`	On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR Marco Loog, Jesse H. Krijthe, Are Charles Jensen
2017	J	`jnl`	On reducing sampling variance in covariate shift using control variates. CoRR Wouter M. Kouw, Marco Loog
2017	J	`jnl`	Projected estimators for robust semi-supervised classification. Mach. Learn. Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	Scale-Regularized Filter Learning. CoRR Marco Loog, François Lauze
2017	J	`jnl`	Supervised Classification: Quite a Brief Overview. CoRR Marco Loog
2017	A	`conf`	Supervised Scale-Regularized Linear Convolutionary Filters. BMVC Marco Loog, François Lauze
2017	J	`jnl`	Target contrastive pessimistic risk for robust domain adaptation. CoRR Wouter M. Kouw, Marco Loog
2016	C	`conf`	A Compact Representation of Multiscale Dissimilarity Data by Prototype Selection. CIARP Yenisel Plasencia Calana, Yan Li, Robert P. W. Duin, Mauricio Orozco-Alzate, Marco Loog, Edel B. García Reyes
2016	B	`conf`	A soft-labeled self-training approach. ICPR Alexander Mey, Marco Loog
2016	B	`conf`	Active learning using uncertainty information. ICPR Yazhou Yang, Marco Loog
2016	B	`conf`	An empirical investigation into the inconsistency of sequential active learning. ICPR Marco Loog, Yazhou Yang
2016	J	`jnl`	Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Trans. Pattern Anal. Mach. Intell. Marco Loog
2016	—	`ed.`	Deep Learning and Data Labeling for Medical Applications - First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings LABELS/DLMIA@MICCAI Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew P. Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise
2016	J	`jnl`	Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. Veronika Cheplygina, David M. J. Tax, Marco Loog
2016	J	`jnl`	Feature-Level Domain Adaptation. J. Mach. Learn. Res. Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog
2016	—	`conf`	Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings. EuroMed (1) Yuan Zeng, Jiexiong Tang, Jan C. A. van der Lubbe, Marco Loog
2016	—	`ch.`	On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. Handbook of Pattern Recognition and Computer Vision Marco Loog, Jesse H. Krijthe, Are Charles Jensen
2016	J	`jnl`	On Regularization Parameter Estimation under Covariate Shift. CoRR Wouter M. Kouw, Marco Loog
2016	B	`conf`	On regularization parameter estimation under covariate shift. ICPR Wouter M. Kouw, Marco Loog
2016	J	`jnl`	Optimistic Semi-supervised Least Squares Classification. CoRR Jesse H. Krijthe, Marco Loog
2016	B	`conf`	Optimistic semi-supervised least squares classification. ICPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	Projected Estimators for Robust Semi-supervised Classification. CoRR Jesse H. Krijthe, Marco Loog
2016	—	`conf`	Reproducible Pattern Recognition Research: The Case of Optimistic SSL. RRPR@ICPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	Reproducible Pattern Recognition Research: The Case of Optimistic SSL. CoRR Jesse H. Krijthe, Marco Loog
2016	—	`ed.`	Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings S+SSPR Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard C. Wilson
2016	J	`jnl`	Template Matching on the Roto-Translation Group. CoRR Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits
2016	—	`conf`	The Peaking Phenomenon in Semi-supervised Learning. S+SSPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	The Peaking Phenomenon in Semi-supervised Learning. CoRR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR Jesse H. Krijthe, Marco Loog
2016	B	`conf`	Weighted K-Nearest Neighbor revisited. ICPR Manuele Bicego, Marco Loog
2015	J	`jnl`	Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. CoRR Marco Loog
2015	J	`jnl`	Feature-Level Domain Adaptation. CoRR Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten
2015	J	`jnl`	Implicitly Constrained Semi-Supervised Least Squares Classification. CoRR Jesse H. Krijthe, Marco Loog
2015	B	`conf`	Implicitly Constrained Semi-supervised Least Squares Classification. IDA Jesse H. Krijthe, Marco Loog
2015	—	`conf`	Label Stability in Multiple Instance Learning. MICCAI (1) Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
2015	J	`jnl`	Multiple instance learning with bag dissimilarities. Pattern Recognit. Veronika Cheplygina, David M. J. Tax, Marco Loog
2015	J	`jnl`	On classification with bags, groups and sets. Pattern Recognit. Lett. Veronika Cheplygina, David M. J. Tax, Marco Loog
2015	J	`jnl`	Robust Semi-supervised Least Squares Classification by Implicit Constraints. CoRR Jesse H. Krijthe, Marco Loog
2015	J	`jnl`	Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood. IEEE Trans. Neural Networks Learn. Syst. Marco Loog, Are Charles Jensen
2015	—	`ed.`	Similarity-Based Pattern Recognition - Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015, Proceedings SIMBAD Aasa Feragen, Marcello Pelillo, Marco Loog
2015	J	`jnl`	Single- vs. multiple-instance classification. Pattern Recognit. Ethem Alpaydin, Veronika Cheplygina, Marco Loog, David M. J. Tax
2014	B	`conf`	Classification of COPD with Multiple Instance Learning. ICPR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Johannes Holst Pedersen, Marco Loog, Marleen de Bruijne
2014	J	`jnl`	Dissimilarity-based Ensembles for Multiple Instance Learning. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2014	J	`jnl`	Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. CoRR Jesse H. Krijthe, Marco Loog
2014	B	`conf`	Implicitly Constrained Semi-supervised Linear Discriminant Analysis. ICPR Jesse H. Krijthe, Marco Loog
2014	—	`conf`	Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. S+SSPR Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, Marco Loog
2014	—	`conf`	Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder. MLMI Veronika Cheplygina, David M. J. Tax, Marco Loog, Aasa Feragen
2014	J	`jnl`	On Classification with Bags, Groups and Sets. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2014	J	`jnl`	Quantile Representation for Indirect Immunofluorescence Image Classification. CoRR David M. J. Tax, Veronika Cheplygina, Marco Loog
2014	J	`jnl`	Semi-hidden target recognition in gated viewer images fused with thermal IR images. Inf. Fusion Menno A. Smeelen, Piet B. W. Schwering, Alexander Toet, Marco Loog
2014	J	`jnl`	Semi-supervised linear discriminant analysis through moment-constraint parameter estimation. Pattern Recognit. Lett. Marco Loog
2014	—	`ed.`	Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings S+SSPR Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo
2014	—	`conf`	Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images. EMMCVPR Erik J. Bekkers, Remco Duits, Marco Loog
2013	—	`conf`	Combining Instance Information to Classify Bags. MCS Veronika Cheplygina, David M. J. Tax, Marco Loog
2013	—	`conf`	Conditional Random Fields for Protein Function Prediction. PRIB Thies Gehrmann, Marco Loog, Marcel J. T. Reinders, Dick de Ridder
2013	J	`jnl`	FIDOS: A generalized Fisher based feature extraction method for domain shift. Pattern Recognit. Viet Cuong Dinh, Robert P. W. Duin, Ignacio Piqueras-Salazar, Marco Loog
2013	—	`conf`	Femoral cartilage segmentation in Knee MRI scans using two stage voxel classification. EMBC Adhish Prasoon, Christian Igel, Marco Loog, François Lauze, Erik B. Dam, Mads Nielsen
2013	J	`jnl`	Multi-spectral video endoscopy system for the detection of cancerous tissue. Pattern Recognit. Lett. Raimund Leitner, Martin De Biasio, Thomas Arnold, Viet Cuong Dinh, Marco Loog, Robert P. W. Duin
2013	J	`jnl`	Multiple Instance Learning with Bag Dissimilarities. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2013	J	`jnl`	Multiple-instance learning as a classifier combining problem. Pattern Recognit. Yan Li, David M. J. Tax, Robert P. W. Duin, Marco Loog
2013	—	`ch.`	Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness. Similarity-Based Pattern Analysis and Recognition Robert P. W. Duin, Elzbieta Pekalska, Marco Loog
2013	—	`conf`	On the Informativeness of Asymmetric Dissimilarities. SIMBAD Yenisel Plasencia Calana, Veronika Cheplygina, Robert P. W. Duin, Edel B. García Reyes, Mauricio Orozco-Alzate, David M. J. Tax, Marco Loog
2013	J	`jnl`	Stratified Generalized Procrustes Analysis. Int. J. Comput. Vis. Adrien Bartoli, Daniel Pizarro, Marco Loog
2013	—	`conf`	The Link between Multiple-Instance Learning and Learning from Only Positive and Unlabelled Examples. MCS Yan Li, David M. J. Tax, Robert P. W. Duin, Marco Loog
2012	B	`conf`	A study on semi-supervised dissimilarity representation. ICPR Viet Cuong Dinh, Robert P. W. Duin, Marco Loog
2012	—	`conf`	Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology. MICCAI (3) Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog
2012	B	`conf`	Automated classification of local patches in colon histopathology. ICPR Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog
2012	J	`jnl`	Bridging Structure and Feature Representations in Graph Matching. Int. J. Pattern Recognit. Artif. Intell. Wan-Jui Lee, Veronika Cheplygina, David M. J. Tax, Marco Loog, Robert P. W. Duin
2012	B	`conf`	Cascaded classifier for large-scale data applied to automatic segmentation of articular cartilage. Image Processing Adhish Prasoon, Christian Igel, Marco Loog, François Lauze, Erik Dam, Mads Nielsen
2012	—	`conf`	Class-Dependent Dissimilarity Measures for Multiple Instance Learning. SSPR/SPR Veronika Cheplygina, David M. J. Tax, Marco Loog
2012	B	`conf`	Combining multi-scale dissimilarities for image classification. ICPR Yan Li, Robert P. W. Duin, Marco Loog
2012	—	`conf`	Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis. SSPR/SPR Marco Loog, Are Charles Jensen
2012	B	`conf`	Does one rotten apple spoil the whole barrel? ICPR Veronika Cheplygina, David M. J. Tax, Marco Loog
2012	B	`conf`	Improving cross-validation based classifier selection using meta-learning. ICPR Jesse H. Krijthe, Tin Kam Ho, Marco Loog
2012	B	`conf`	Metric learning by directly minimizing the k-NN training error. ICPR Konstantin Chernoff, Marco Loog, Mads Nielsen
2012	—	`conf`	Mode Seeking Clustering by KNN and Mean Shift Evaluated. SSPR/SPR Robert P. W. Duin, Ana L. N. Fred, Marco Loog, Elzbieta Pekalska
2012	—	`conf`	Nearest neighbor-based importance weighting. MLSP Marco Loog
2012	J	`jnl`	Scale selection for supervised image segmentation. Image Vis. Comput. Yan Li, David M. J. Tax, Marco Loog
2012	B	`conf`	Scale-invariant sampling for supervised image segmentation. ICPR Yan Li, Marco Loog
2012	—	`conf`	Supervised localization of cell nuclei on TMA images. MMBIA Alessandro Ibba, Robert P. W. Duin, Marco Loog
2012	—	`conf`	The Dipping Phenomenon. SSPR/SPR Marco Loog, Robert P. W. Duin
2012	—	`conf`	Towards exaggerated emphysema stereotypes. Computer-Aided Diagnosis C. Chen, Lauge Sørensen, François Lauze, Christian Igel, Marco Loog, Aasa Feragen, Marleen de Bruijne, Mads Nielsen
2012	B	`conf`	Training data selection for cancer detection in multispectral endoscopy images. ICPR Viet Cuong Dinh, Marco Loog, Raimund Leitner, Olga Rajadell, Robert P. W. Duin
2011	—	`conf`	Bag Dissimilarities for Multiple Instance Learning. SIMBAD David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee
2011	J	`jnl`	Dissimilarity-based detection of schizophrenia. Int. J. Imaging Syst. Technol. Aydin Ulas, Robert P. W. Duin, Umberto Castellani, Marco Loog, Pasquale Mirtuono, Manuele Bicego, Vittorio Murino, Marcella Bellani, Stefania Cerruti, Michele Tansella, Paolo Brambilla
2011	—	`conf`	Forming Different-Complexity Covariance-Model Subspaces through Piecewise-Constant Spectra for Hyperspectral Image Classification. SCIA Are Charles Jensen, Marco Loog
2011	—	`conf`	Generalized Augmentation of Multiple Kernels. MCS Wan-Jui Lee, Robert P. W. Duin, Marco Loog
2011	—	`conf`	Information theoretic preattentive saliency: A closed-form solution. ICCV Workshops Marco Loog
2011	J	`jnl`	On Combining Computer-Aided Detection Systems. IEEE Trans. Medical Imaging Meindert Niemeijer, Marco Loog, Michael D. Abràmoff, Max A. Viergever, Mathias Prokop, Bram van Ginneken
2011	—	`ed.`	Pattern Recognition in Bioinformatics - 6th IAPR International Conference, PRIB 2011, Delft, The Netherlands, November 2-4, 2011. Proceedings PRIB Marco Loog, Lodewyk F. A. Wessels, Marcel J. T. Reinders, Dick de Ridder
2011	—	`conf`	Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features. TSD Babak Loni, Gijs van Tulder, Pascal Wiggers, David M. J. Tax, Marco Loog
2011	J	`jnl`	SEDMI: Saliency based edge detection in multispectral images. Image Vis. Comput. Viet Cuong Dinh, Raimund Leitner, Pavel Paclík, Marco Loog, Robert P. W. Duin
2011	—	`conf`	Semi-supervised Linear Discriminant Analysis Using Moment Constraints. PSL Marco Loog
2011	—	`conf`	Supervised Scale-Invariant Segmentation (and Detection). SSVM Yan Li, David M. J. Tax, Marco Loog
2011	—	`conf`	Towards exaggerated image stereotypes. ACPR Chen Chen, François Lauze, Christian Igel, Aasa Feragen, Marco Loog, Mads Nielsen
2010	—	`conf`	A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. MICCAI (3) Mehrdad J. Gangeh, Lauge Sørensen, Saher B. Shaker, Mohamed S. Kamel, Marleen de Bruijne, Marco Loog
2010	J	`jnl`	A framework for optimizing measurement weight maps to minimize the required sample size. Medical Image Anal. Arish A. Qazi, Dan R. Jørgensen, Martin Lillholm, Marco Loog, Mads Nielsen, Erik B. Dam
2010	—	`conf`	An experimental study on combining Euclidean distances. CIP Wan-Jui Lee, Robert P. W. Duin, Alessandro Ibba, Marco Loog
2010	—	`conf`	Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier. ECML/PKDD (2) Marco Loog
2010	—	`conf`	Dissimilarity-Based Multiple Instance Learning. SSPR/SPR Lauge Sørensen, Marco Loog, David M. J. Tax, Wan-Jui Lee, Marleen de Bruijne, Robert P. W. Duin
2010	—	`conf`	Early diagnosis of dementia based on intersubject whole-brain dissimilarities. ISBI Stefan Klein, Marco Loog, Fedde van der Lijn, Tom den Heijer, Alexander Hammers, Marleen de Bruijne, Aad van der Lugt, Robert P. W. Duin, Monique M. B. Breteler, Wiro J. Niessen
2010	—	`conf`	Feature-Based Dissimilarity Space Classification. ICPR Contests Robert P. W. Duin, Marco Loog, Elzbieta Pekalska, David M. J. Tax
2010	—	`conf`	Image Dissimilarity-Based Quantification of Lung Disease from CT. MICCAI (1) Lauge Sørensen, Marco Loog, Pechin Lo, Haseem Ashraf, Asger Dirksen, Robert P. W. Duin, Marleen de Bruijne
2010	J	`jnl`	Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy. IEEE Trans. Medical Imaging Ihor Smal, Marco Loog, Wiro J. Niessen, Erik Meijering
2010	A	`conf`	Stratified Generalized Procrustes Analysis. BMVC Adrien Bartoli, Daniel Pizarro, Marco Loog
2010	J	`jnl`	The Improbability of Harris Interest Points. IEEE Trans. Pattern Anal. Mach. Intell. Marco Loog, François Lauze
2010	J	`jnl`	Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote. Sens. Are Charles Jensen, Marco Loog, Anne H. Schistad Solberg
2009	—	`conf`	Bicycle chain shape models. CVPR Workshops Stefan Sommer, Aditya Tatu, Chen Chen, D. R. Jurgensen, Marleen de Bruijne, Marco Loog, Mads Nielsen, François Lauze
2009	—	`conf`	Dense iterative contextual pixel classification using Kriging. CVPR Workshops Melanie Ganz, Marco Loog, Sami S. Brandt, Mads Nielsen
2009	—	`conf`	Discriminative Shape Alignment. IPMI Marco Loog, Marleen de Bruijne
2009	—	`conf`	Maximum Membership Scale Selection. MCS Marco Loog, Yan Li, David M. J. Tax
2009	—	`conf`	Optimal Mean-Precision Classifier. MCS David M. J. Tax, Marco Loog, Robert P. W. Duin
2009	—	`conf`	Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging. ISBI Ihor Smal, Marco Loog, Wiro J. Niessen, Erik Meijering
2008	J	`jnl`	A note on an extreme case of the generalized optimal discriminant transformation. Neurocomputing Marco Loog, Xiaojun Wu, Jieping Lu, Jing-Yu Yang, Shitong Wang, Josef Kittler
2008	B	`conf`	A variational method for automatic localization of the most pathological ROI in the knee cartilage. Image Processing Arish A. Qazi, Erik B. Dam, Marco Loog, Mads Nielsen, François Lauze, Claus Christiansen
2008	J	`jnl`	Automated Effect-Specific Mammographic Pattern Measures. IEEE Trans. Medical Imaging Jakob Raundahl, Marco Loog, Paola Pettersen, László B. Tankó, Mads Nielsen
2008	J	`jnl`	Efficient Segmentation by Sparse Pixel Classification. IEEE Trans. Medical Imaging Erik B. Dam, Marco Loog
2008	J	`jnl`	On Distributional Assumptions and Whitened Cosine Similarities. IEEE Trans. Pattern Anal. Mach. Intell. Marco Loog
2008	J	`jnl`	Second Order Structure of Scale-Space Measurements. J. Math. Imaging Vis. Bo Markussen, Kim Steenstrup Pedersen, Marco Loog
2008	—	`ed.`	Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings SSPR/SPR Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog
2007	J	`jnl`	A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. J. Mach. Learn. Res. Marco Loog
2007	—	`conf`	A Family of Principal Component Analyses for Dealing with Outliers. MICCAI (2) Juan Eugenio Iglesias, Marleen de Bruijne, Marco Loog, François Lauze, Mads Nielsen
2007	—	`ed.`	Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings ICIC (2) De-Shuang Huang, Laurent Heutte, Marco Loog
2007	—	`ed.`	Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings ICIC (3) De-Shuang Huang, Laurent Heutte, Marco Loog
2007	—	`ed.`	Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings ICIC (1) De-Shuang Huang, Laurent Heutte, Marco Loog
2007	—	`conf`	Blur Invariant Image Priors. SSVM Marco Loog, François Lauze
2007	B	`conf`	Evaluation of four mammographic density measures on HRT data. Image Processing Jakob Raundahl, Marco Loog, Paola Pettersen, Mads Nielsen
2007	—	`conf`	Generic Maximum Likely Scale Selection. SSVM Kim Steenstrup Pedersen, Marco Loog, Bo Markussen
2007	—	`conf`	Localized Maximum Entropy Shape Modelling. IPMI Marco Loog
2007	J	`jnl`	On an alternative formulation of the Fisher criterion that overcomes the small sample problem. Pattern Recognit. Marco Loog
2007	—	`conf`	Quantifying Effect-Specific Mammographic Density. MICCAI (2) Jakob Raundahl, Marco Loog, Paola Pettersen, Mads Nielsen
2007	—	`conf`	Salient Point and Scale Detection by Minimum Likelihood. Gaussian Processes in Practice Kim Steenstrup Pedersen, Marco Loog, Pieter van Dorst
2007	—	`conf`	The Jet Metric. SSVM Marco Loog
2006	J	`jnl`	A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Medical Image Anal. Arnold M. R. Schilham, Bram van Ginneken, Marco Loog
2006	—	`conf`	Bony Structure Suppression in Chest Radiographs. CVAMIA Marco Loog, Bram van Ginneken
2006	—	`conf`	Conditional Linear Discriminant Analysis. ICPR (2) Marco Loog
2006	—	`conf`	Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis. ICPR (3) A. K. Qin, Ponnuthurai N. Suganthan, Marco Loog
2006	J	`jnl`	Filter learning: Application to suppression of bony structures from chest radiographs. Medical Image Anal. Marco Loog, Bram van Ginneken, Arnold M. R. Schilham
2006	J	`jnl`	Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction. Pattern Recognit. A. K. Qin, Ponnuthurai N. Suganthan, Marco Loog
2006	—	`conf`	Generic Blind Source Separation Using Second-Order Local Statistics. SSPR/SPR Marco Loog
2006	—	`conf`	Local Discriminant Analysis. ICPR (3) Marco Loog, Dick de Ridder
2006	B	`conf`	Mammographic density measured as changes in tissue structure caused by HRT. Image Processing Jakob Raundahl, Marco Loog, Mads Nielsen
2006	J	`jnl`	Recent submissions in linear dimensionality reduction and face recognition. Pattern Recognit. Lett. Robert P. W. Duin, Marco Loog, Tin Kam Ho
2006	J	`jnl`	Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Anal. Bram van Ginneken, Mikkel B. Stegmann, Marco Loog
2006	J	`jnl`	Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans. Medical Imaging Marco Loog, Bram van Ginneken
2006	—	`conf`	Understanding Hessian-Based Density Scoring. Digital Mammography / IWDM Jakob Raundahl, Marco Loog, Mads Nielsen
2005	—	`conf`	A Scale Invariant Covariance Structure on Jet Space. DSSCV Bo Markussen, Kim Steenstrup Pedersen, Marco Loog
2005	J	`jnl`	Dimensionality reduction of image features using the canonical contextual correlation projection. Pattern Recognit. Marco Loog, Bram van Ginneken, Robert P. W. Duin
2005	A*	`conf`	Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data. AAAI A. K. Qin, S. Y. M. Shi, Ponnuthurai N. Suganthan, Marco Loog
2005	—	`conf`	Maximum Likely Scale Estimation. DSSCV Marco Loog, Kim Steenstrup Pedersen, Bo Markussen
2005	J	`jnl`	Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion. Pattern Recognit. A. K. Qin, Ponnuthurai N. Suganthan, Marco Loog
2004	B	`conf`	Comparative study of retinal vessel segmentation methods on a new publicly available database. Image Processing Meindert Niemeijer, Joes Staal, Bram van Ginneken, Marco Loog, Michael D. Abràmoff
2004	—	`conf`	Dimensionality Reduction by Canonical Contextual Correlation Projections. ECCV (1) Marco Loog, Bram van Ginneken, Robert P. W. Duin
2004	—	`conf`	Integrating Automatic and Interactive Brain Tumor Segmentation. ICPR (3) Erik Dam, Marco Loog, Marloes M. J. Letteboer
2004	J	`jnl`	Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion. IEEE Trans. Pattern Anal. Mach. Intell. Marco Loog, Robert P. W. Duin
2004	—	`conf`	Local Fisher Embedding. ICPR (2) Dick de Ridder, Marco Loog, Marcel J. T. Reinders
2004	—	`conf`	Pixel Position Regression - Application to Medical Image Segmentation. ICPR (3) Bram van Ginneken, Marco Loog
2004	—	`conf`	Static Posterior Probability Fusion for Signal Detection: Applications in the Detection of Interstitial Diseases in Chest Radiographs. ICPR (1) Marco Loog, Bram van Ginneken
2004	—	`—`	Supervised dimensionality reduction and contextual pattern recognition in medical image processing. Marco Loog
2004	—	`conf`	Support Blob Machines. The Sparsification of Linear Scale Space. ECCV (4) Marco Loog
2004	J	`jnl`	The MDF discrimination measure: Fisher in disguise. Neural Networks Marco Loog, Robert P. W. Duin, Max A. Viergever
2003	—	`conf`	Gaussian Scale Space from Insufficient Image Information. Scale-Space Marco Loog, Martin Lillholm, Mads Nielsen, Max A. Viergever
2003	B	`conf`	Interactive shape models. Image Processing Bram van Ginneken, Marleen de Bruijne, Marco Loog, Max A. Viergever
2003	B	`conf`	Model-based segmentation of abdominal aortic aneurysms in CTA images. Image Processing Marleen de Bruijne, Bram van Ginneken, Wiro J. Niessen, Marco Loog, Max A. Viergever
2003	—	`conf`	Multi-scale Nodule Detection in Chest Radiographs. MICCAI (1) Arnold M. R. Schilham, Bram van Ginneken, Marco Loog
2003	B	`conf`	Segmenting the posterior ribs in chest radiographs by iterated contextual pixel classification. Image Processing Marco Loog, Bram van Ginneken, Max A. Viergever
2002	—	`conf`	Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data. SSPR/SPR Marco Loog, Robert P. W. Duin
2002	—	`conf`	Supervised Segmentation by Iterated Contextual Pixel Classification. ICPR (2) Marco Loog, Bram van Ginneken
2001	J	`jnl`	Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. IEEE Trans. Pattern Anal. Mach. Intell. Marco Loog, Robert P. W. Duin, Reinhold Haeb-Umbach
2001	—	`conf`	On the Behavior of Spatial Critical Points under Gaussian Blurring. A Folklore Theorem and Scale-Space Constraints. Scale-Space Marco Loog, Johannes Jisse Duistermaat, Luc Florack
2000	B	`conf`	Multi-Class Linear Feature Extraction by Nonlinear PCA. ICPR Robert P. W. Duin, Marco Loog, Reinhold Haeb-Umbach
2000	A	`conf`	Multi-class linear dimension reduction by generalized Fisher criteria. INTERSPEECH Marco Loog, Reinhold Haeb-Umbach
1999	—	`conf`	An investigation of cepstral parameterisations for large vocabulary speech recognition. EUROSPEECH Reinhold Haeb-Umbach, Marco Loog