Peter Tiño - RankMe

197 papers A* 5A 5B 29C 16Misc 2Journal 90Unranked 46

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Interpretable modelling and visualization of biomedical data. Neurocomputing Sreejita Ghosh, Elizabeth Sarah Baranowski, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte
2022	J	`jnl`	1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments. Astron. Comput. Marco Canducci, Petra Awad, Abolfazl Taghribi, Mohammad Mohammadi, Michele Mastropietro, Sven De Rijcke, Reynier Peletier, Rory Smith, Kerstin Bunte, Peter Tiño
2022	C	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2022 - 23rd International Conference, IDEAL 2022, Manchester, UK, November 24-26, 2022, Proceedings IDEAL Hujun Yin, David Camacho, Peter Tiño
2022	J	`jnl`	Probabilistic modelling of general noisy multi-manifold data sets. Artif. Intell. Marco Canducci, Peter Tiño, Michele Mastropietro
2022	C	`conf`	Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical Systems. BDCAT Hayatullahi Bolaji Adeyemo, Rami Bahsoon, Peter Tiño
2021	J	`jnl`	A Survey on Neural Network Interpretability. IEEE Trans. Emerg. Top. Comput. Intell. Yu Zhang, Peter Tiño, Ales Leonardis, Ke Tang
2021	B	`conf`	Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization. IJCNN Stephen Friess, Peter Tiño, Zhao Xu, Stefan Menzel, Bernhard Sendhoff, Xin Yao
2021	J	`jnl`	Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices. IEEE Trans. Neural Networks Learn. Syst. Fengzhen Tang, Mengling Fan, Peter Tiño
2021	C	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2021 - 22nd International Conference, IDEAL 2021, Manchester, UK, November 25-27, 2021, Proceedings IDEAL Hujun Yin, David Camacho, Peter Tiño, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
2021	B	`conf`	Interpreting Node Embedding with Text-labeled Graphs. IJCNN Giuseppe Serra, Zhao Xu, Mathias Niepert, Carolin Lawrence, Peter Tiño, Xin Yao
2021	J	`jnl`	Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices. CoRR Fengzhen Tang, Haifeng Feng, Peter Tiño, Bailu Si, Daxiong Ji
2020	J	`jnl`	A Geometric Framework for Pitch Estimation on Acoustic Musical Signals. CoRR Tom Goodman, Karoline van Gemst, Peter Tiño
2020	J	`jnl`	A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Seyma Kucukozer Cavdar, Tugba Taskaya-Temizel, Mirco Musolesi, Peter Tiño
2020	J	`jnl`	A Survey on Neural Network Interpretability. CoRR Yu Zhang, Peter Tiño, Ales Leonardis, Ke Tang
2020	B	`conf`	ASAP - A Sub-sampling Approach for Preserving Topological Structures. ESANN Abolfazl Taghribi, Kerstin Bunte, Michele Mastropietro, Sven De Rijcke, Peter Tiño
2020	J	`jnl`	Dynamical Systems as Temporal Feature Spaces. J. Mach. Learn. Res. Peter Tiño
2020	J	`jnl`	Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
2020	—	`conf`	Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances. PPSN (1) Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao
2020	J	`jnl`	Input representation in recurrent neural networks dynamics. CoRR Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tiño
2020	J	`jnl`	LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density. CoRR Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tiño
2020	B	`conf`	Representing Experience in Continuous Evolutionary optimisation through Problem-tailored Search Operators. CEC Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao
2020	J	`jnl`	Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. Frank-Michael Schleif, Christoph Raab, Peter Tiño
2020	B	`conf`	Visualisation and knowledge discovery from interpretable models. IJCNN Sreejita Ghosh, Peter Tiño, Kerstin Bunte
2020	J	`jnl`	Visualisation and knowledge discovery from interpretable models. CoRR Sreejita Ghosh, Peter Tiño, Kerstin Bunte
2019	J	`jnl`	A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks. Evol. Comput. Siang Yew Chong, Peter Tiño, Jun He, Xin Yao
2019	J	`jnl`	A mixture of experts model for predicting persistent weather patterns. CoRR María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, Carlos Casanova-Mateo, Sancho Salcedo-Sanz
2019	J	`jnl`	Coevolutionary systems and PageRank. Artif. Intell. Siang Yew Chong, Peter Tiño, Jun He
2019	J	`jnl`	Dynamical Systems as Temporal Feature Spaces. CoRR Peter Tiño
2019	A*	`conf`	Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. AAAI María Pérez-Ortiz, Peter Tiño, Rafal Mantiuk, César Hervás-Martínez
2019	J	`jnl`	Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. CoRR María Pérez-Ortiz, Peter Tiño, Rafal Mantiuk, César Hervás-Martínez
2019	—	`conf`	Extended stochastic derivative-free optimization on riemannian manifolds. GECCO (Companion) Robert Simon Fong, Peter Tiño
2019	J	`jnl`	Feature Relevance Bounds for Ordinal Regression. CoRR Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
2019	J	`jnl`	Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. CoRR Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tiño, Barbara Hammer
2019	B	`conf`	Feature relevance bounds for ordinal regression. ESANN Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer
2019	—	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part I IDEAL (1) Hujun Yin, David Camacho, Peter Tiño, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger
2019	—	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part II IDEAL (2) Hujun Yin, David Camacho, Peter Tiño, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger
2019	—	`conf`	Learning Transferable Variation Operators in a Continuous Genetic Algorithm. SSCI Stephen Friess, Peter Tiño, Stefan Menzel, Bernhard Sendhoff, Xin Yao
2019	—	`conf`	Multiobjective Optimization Approach to Localization of Ectopic Beats by Single Dipole: Case Study. CinC Jana Svehlíková, Jan Zelinka, Milan Tysler, Peter Tiño
2019	J	`jnl`	Self-awareness in Software Engineering: A Systematic Literature Review. ACM Trans. Auton. Adapt. Syst. Abdessalam Elhabbash, Maria Salama, Rami Bahsoon, Peter Tiño
2018	B	`conf`	A mixture of experts model for predicting persistent weather patterns. IJCNN M. Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, Carlos Casanova-Mateo, Sancho Salcedo-Sanz
2018	J	`jnl`	Asymptotic Fisher memory of randomized linear symmetric Echo State Networks. Neurocomputing Peter Tiño
2018	B	`conf`	Machine learning and data analysis in astroinformatics. ESANN Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño
2018	B	`conf`	Randomized Recurrent Neural Networks. ESANN Claudio Gallicchio, Alessio Micheli, Peter Tiño
2018	—	`conf`	Sparsification of Indefinite Learning Models. S+SSPR Frank-Michael Schleif, Christoph Raab, Peter Tiño
2018	J	`jnl`	Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2017	B	`conf`	Classification of sparsely and irregularly sampled time series: A learning in model space approach. IJCNN Yuan Shen, Peter Tiño, Krasimira Tsaneva-Atanasova
2017	B	`conf`	Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. ESANN Sreejita Ghosh, Elizabeth Sarah Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tiño, Kerstin Bunte
2017	B	`conf`	Fisher memory of linear Wigner echo state networks. ESANN Peter Tiño
2017	J	`jnl`	Indefinite Core Vector Machine. Pattern Recognit. Frank-Michael Schleif, Peter Tiño
2017	B	`conf`	Linear dynamical based models for sequential domains. IJCNN Luca Pasa, Alessandro Sperduti, Peter Tiño
2017	J	`jnl`	Linking Twitter Events With Stock Market Jitters. CoRR Fani Tsapeli, Nikolaos Bezirgiannidis, Peter Tiño, Mirco Musolesi
2017	J	`jnl`	Ordinal regression based on learning vector quantization. Neural Networks Fengzhen Tang, Peter Tiño
2017	B	`conf`	Probabilistic matching: Causal inference under measurement errors. IJCNN Fani Tsapeli, Peter Tiño, Mirco Musolesi
2017	A	`conf`	Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services. ICWS Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño
2016	J	`jnl`	Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment. Frontiers Comput. Neurosci. Hanin H. Alahmadi, Yuan Shen, Shereen Fouad, Caroline Di Bernardi Luft, Peter Bentham, Zoe Kourtzi, Peter Tiño
2016	—	`conf`	Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR Frank-Michael Schleif, Ata Kabán, Peter Tiño
2016	—	`conf`	Interaction-Awareness for Self-Adaptive Volunteer Computing. SASO Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño
2016	B	`conf`	Learning in indefinite proximity spaces - recent trends. ESANN Frank-Michael Schleif, Peter Tiño, Yingyu Liang
2016	J	`jnl`	Model-Coupled Autoencoder for Time Series Visualisation. CoRR Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer
2016	J	`jnl`	Model-coupled autoencoder for time series visualisation. Neurocomputing Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer
2016	J	`jnl`	Oversampling the Minority Class in the Feature Space. IEEE Trans. Neural Networks Learn. Syst. María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez
2016	C	`conf`	Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams. IDEAL Sultanah Al Otaibi, Peter Tiño, Somak Raychaudhury
2016	J	`jnl`	Probabilistic classifiers with low rank indefinite kernels. CoRR Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2016	—	`conf`	Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data. WSOM Nahed Alowadi, Yuan Shen, Peter Tiño
2015	—	`ch.`	Artificial Neural Network Models. Handbook of Computational Intelligence Peter Tiño, Lubica Benuskova, Alessandro Sperduti
2015	J	`jnl`	Autoencoding Time Series for Visualisation. CoRR Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra
2015	B	`conf`	Autoencoding time series for visualisation. ESANN Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra
2015	—	`conf`	Automated Detection of Galaxy Groups Through Probabilistic Hough Transform. ICONIP (3) Rafee T. Ibrahem, Peter Tiño, Richard J. Pearson, Trevor J. Ponman, Arif Babul
2015	B	`conf`	Incremental probabilistic classification vector machine with linear costs. IJCNN Frank-Michael Schleif, H. Chen, Peter Tiño
2015	J	`jnl`	Indefinite Proximity Learning: A Review. Neural Comput. Frank-Michael Schleif, Peter Tiño
2015	—	`conf`	Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2015	A*	`conf`	Model Metric Co-Learning for Time Series Classification. IJCAI Huanhuan Chen, Fengzhen Tang, Peter Tiño, Anthony G. Cohn, Xin Yao
2015	B	`conf`	Probabilistic Classification Vector Machine at large scale. ESANN Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2015	A	`conf`	Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness. ICWS Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño, Peter R. Lewis
2015	J	`jnl`	The Benefits of Modeling Slack Variables in SVMs. Neural Comput. Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen
2014	Misc	`conf`	A Utility Model for Volunteered Service Composition. UCC Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño, Peter R. Lewis
2014	J	`jnl`	Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space. Comput. Chem. Eng. Huanhuan Chen, Peter Tiño, Xin Yao
2014	J	`jnl`	Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network. Eng. Appl. Artif. Intell. Joseba Quevedo, Huanhuan Chen, Miquel Àngel Cugueró, Peter Tiño, Vicenç Puig, Diego García, Ramon Sarrate, Xin Yao
2014	J	`jnl`	Dimensionality reduction and topographic mapping of binary tensors. Pattern Anal. Appl. Jakub Mazgut, Peter Tiño, Mikael Bodén, Hong Yan
2014	J	`jnl`	Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection. IEEE Trans. Neural Networks Learn. Syst. Huanhuan Chen, Peter Tiño, Xin Yao
2014	J	`jnl`	Learning in the Model Space for Cognitive Fault Diagnosis. IEEE Trans. Neural Networks Learn. Syst. Huanhuan Chen, Peter Tiño, Ali Rodan, Xin Yao
2014	B	`conf`	Learning the deterministically constructed Echo State Networks. IJCNN Fengzhen Tang, Peter Tiño, Huanhuan Chen
2014	J	`jnl`	Ordinal regression neural networks based on concentric hyperspheres. Neural Networks Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez
2014	B	`conf`	Recent trends in learning of structured and non-standard data. ESANN Frank-Michael Schleif, Peter Tiño, Thomas Villmann
2014	J	`jnl`	Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models. NeuroImage Yuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tiño
2014	B	`conf`	Support Vector Ordinal Regression using Privileged Information. ESANN Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen
2014	—	`conf`	Towards Self-Aware Service Composition. HPCC/CSS/ICESS Abdessalam Elhabbash, Rami Bahsoon, Peter Tiño
2013	J	`jnl`	A Framework for the Analysis of Process Mining Algorithms. IEEE Trans. Syst. Man Cybern. Syst. Philip Weber, Behzad Bordbar, Peter Tiño
2013	—	`conf`	A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data. MICCAI (2) Yuan Shen, Stephen D. Mayhew, Zoe Kourtzi, Peter Tiño
2013	C	`conf`	A principled approach to mining from noisy logs using Heuristics Miner. CIDM Phil Weber, Behzad Bordbar, Peter Tiño
2013	J	`jnl`	Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models. IEEE Trans. Biomed. Eng. Orla M. Doyle, Krasimira Tsaneva-Atanasova, James Michael Harte, Paul A. Tiffin, Peter Tiño, Vanessa Díaz-Zuccarini
2013	J	`jnl`	Complex Coevolutionary Dynamics - Structural Stability and Finite Population Effects. IEEE Trans. Evol. Comput. Peter Tiño, Siang Yew Chong, Xin Yao
2013	B	`conf`	Concept drift detection for online class imbalance learning. IJCNN Shuo Wang, Leandro L. Minku, Davide Ghezzi, Daniele Caltabiano, Peter Tiño, Xin Yao
2013	J	`jnl`	Degree distribution and scaling in the Connecting Nearest Neighbors model CoRR Boris Rudolf, Mária Markosová, Martin Cajági, Peter Tiño
2013	J	`jnl`	Exploitation of Pairwise Class Distances for Ordinal Classification. Neural Comput. Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez
2013	J	`jnl`	Incorporating Privileged Information Through Metric Learning. IEEE Trans. Neural Networks Learn. Syst. Shereen Fouad, Peter Tiño, Somak Raychaudhury, Petra Schneider
2013	A*	`conf`	Model-based kernel for efficient time series analysis. KDD Huanhuan Chen, Fengzhen Tang, Peter Tiño, Xin Yao
2013	J	`jnl`	Novel approaches in machine learning and computational intelligence. Neurocomputing Alessio Micheli, Frank-Michael Schleif, Peter Tiño
2013	B	`conf`	Ordinal-based metric learning for learning using privileged information. IJCNN Shereen Fouad, Peter Tiño
2013	J	`jnl`	Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get? Entropy Peter Tiño
2013	J	`jnl`	Scaling Up Estimation of Distribution Algorithms for Continuous Optimization. IEEE Trans. Evol. Comput. Weishan Dong, Tianshi Chen, Peter Tiño, Xin Yao
2013	J	`jnl`	Short term memory in input-driven linear dynamical systems. Neurocomputing Peter Tiño, Ali Rodan
2013	J	`jnl`	Time-dependent series variance learning with recurrent mixture density networks. Neurocomputing Nikolay I. Nikolaev, Peter Tiño, Evgueni N. Smirnov
2012	J	`jnl`	Adaptive Metric Learning Vector Quantization for Ordinal Classification. Neural Comput. Shereen Fouad, Peter Tiño
2012	—	`conf`	Computational Intelligence in Astronomy - A Win-Win Situation. TPNC Peter Tiño, Somak Raychaudhury
2012	J	`jnl`	Improving Generalization Performance in Co-Evolutionary Learning. IEEE Trans. Evol. Comput. Siang Yew Chong, Peter Tiño, Day Chyi Ku, Xin Yao
2012	—	`conf`	Learning Using Privileged Information in Prototype Based Models. ICANN (2) Shereen Fouad, Peter Tiño, Somak Raychaudhury, Petra Schneider
2012	J	`jnl`	Learning in the Model Space for Fault Diagnosis CoRR Huanhuan Chen, Peter Tiño, Xin Yao, Ali Rodan
2012	B	`conf`	Process Mining in Non-Stationary Environments. ESANN Phil Weber, Peter Tiño, Behzad Bordbar
2012	C	`conf`	Prototype Based Modelling for Ordinal Classification. IDEAL Shereen Fouad, Peter Tiño
2012	B	`conf`	Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems. ESANN Peter Tiño, Ali Rodan
2012	J	`jnl`	Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps. Neural Comput. Ali Rodan, Peter Tiño
2012	J	`jnl`	Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks. Mob. Networks Appl. Richard Price, Peter Tiño, Georgios Theodoropoulos
2012	B	`conf`	Theory of Input Driven Dynamical Systems. ESANN Manjunath Gandhi, Peter Tiño, Herbert Jaeger
2011	C	`conf`	A Principled Approach to the Analysis of Process Mining Algorithms. IDEAL Phil Weber, Behzad Bordbar, Peter Tiño
2011	J	`jnl`	Minimum Complexity Echo State Network. IEEE Trans. Neural Networks Ali Rodan, Peter Tiño
2011	B	`conf`	Negatively Correlated Echo State Networks. ESANN Ali Rodan, Peter Tiño
2011	—	`conf`	One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression. ISNN (2) Peter Tiño
2011	—	`conf`	Real-Time Detection of Process Change using Process Mining. ICCSW Phil Weber, Behzad Bordbar, Peter Tiño
2011	J	`jnl`	Scaling Up Estimation of Distribution Algorithms For Continuous Optimization CoRR Weishan Dong, Tianshi Chen, Peter Tiño, Xin Yao
2011	J	`jnl`	Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform. IEEE ACM Trans. Comput. Biol. Bioinform. Peter Tiño, Hongya Zhao, Hong Yan
2011	—	`conf`	Time-Dependent Series Variance Estimation via Recurrent Neural Networks. ICANN (1) Nikolay I. Nikolaev, Peter Tiño, Evgueni N. Smirnov
2011	—	`conf`	Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis. MEMICS Jakub Mazgut, Martina Paulinyová, Peter Tiño
2010	—	`conf`	Adapting to NAT timeout values in P2P overlay networks. IPDPS Workshops Richard Price, Peter Tiño
2010	C	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2010, 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings IDEAL Colin Fyfe, Peter Tiño, Darryl Charles, César Ignacio García-Osorio, Hujun Yin
2010	—	`conf`	Multilinear Decomposition and Topographic Mapping of Binary Tensors. ICANN (1) Jakub Mazgut, Peter Tiño, Mikael Bodén, Hong Yan
2010	C	`conf`	On Reliability Of Simulations Of Complex Co-Evolutionary Processes. ECMS Peter Tiño, Siang Yew Chong, Xin Yao
2010	—	`conf`	One-shot Learning of Poisson Distributions in fast changing environments. Learning paradigms in dynamic environments Peter Tiño
2010	C	`conf`	Simple Deterministically Constructed Recurrent Neural Networks. IDEAL Ali Rodan, Peter Tiño
2010	J	`jnl`	Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application. Pattern Recognit. Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury, Xin Yao, Markus Harva
2009	J	`jnl`	Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. BMC Bioinform. Peter Tiño
2009	—	`conf`	Estimating Time Delay in Gravitationally Lensed Fluxes. Similarity-based learning on structures Peter Tiño, Juan Carlos Cuevas-Tello, Somak Raychaudhury
2009	B	`conf`	Fast parzen window density estimator. IJCNN Xiaoxia Wang, Peter Tiño, Mark A. Fardal, Somak Raychaudhury, Arif Babul
2009	J	`jnl`	Predictive Ensemble Pruning by Expectation Propagation. IEEE Trans. Knowl. Data Eng. Huanhuan Chen, Peter Tiño, Xin Yao
2009	J	`jnl`	Probabilistic Classification Vector Machines. IEEE Trans. Neural Networks Huanhuan Chen, Peter Tiño, Xin Yao
2009	—	`conf`	Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data. IJCBS Peter Tiño, Hongya Zhao, Hong Yan
2009	J	`jnl`	Relationship Between Generalization and Diversity in Coevolutionary Learning. IEEE Trans. Comput. Intell. AI Games Siang Yew Chong, Peter Tiño, Xin Yao
2009	C	`conf`	Still alive: Extending keep-alive intervals in P2P overlay networks. CollaborateCom Richard Price, Peter Tiño
2009	—	`conf`	Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. ICANN (1) Nikolaos Gianniotis, Peter Tiño, Steve Spreckley, Somak Raychaudhury
2009	J	`jnl`	Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application CoRR Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury, Xin Yao, Markus Harva
2009	—	`conf`	Visualization of Structured Data via Generative Probabilistic Modeling. Similarity-Based Clustering Nikolaos Gianniotis, Peter Tiño
2008	—	`conf`	Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Recurrent Neural Networks Peter Tiño
2008	J	`jnl`	Measuring Generalization Performance in Coevolutionary Learning. IEEE Trans. Evol. Comput. Siang Yew Chong, Peter Tiño, Xin Yao
2008	—	`conf`	Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model. ECML/PKDD (2) Xiaoxia Wang, Peter Tiño, Mark A. Fardal
2008	—	`conf`	Predictive Modeling with Echo State Networks. ICANN (1) Michal Cernanský, Peter Tiño
2008	J	`jnl`	Visualization of Tree-Structured Data Through Generative Topographic Mapping. IEEE Trans. Neural Networks Nikolaos Gianniotis, Peter Tiño
2007	—	`conf`	Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks. ICONIP (1) Peter Tiño
2007	—	`conf`	Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences. ICANN (1) Michal Cernanský, Peter Tiño
2007	J	`jnl`	Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Neural Comput. Peter Tiño
2007	C	`ed.`	Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings IDEAL Hujun Yin, Peter Tiño, Emilio Corchado, William Byrne, Xin Yao
2007	—	`ch.`	Markovian Bias of Neural-based Architectures With Feedback Connections. Perspectives of Neural-Symbolic Integration Peter Tiño, Barbara Hammer, Mikael Bodén
2007	A*	`conf`	Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. IJCAI Peter Tiño, Nikolaos Gianniotis
2007	—	`conf`	On Conditions for Intermittent Search in Self-organizing Neural Networks. MICAI Peter Tiño
2007	B	`conf`	Visualisation of tree-structured data through generative probabilistic modelling. ESANN Nikolaos Gianniotis, Peter Tiño
2006	—	`conf`	A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses. ECML Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury
2006	—	`conf`	A Probabilistic Ensemble Pruning Algorithm. ICDM Workshops Huanhuan Chen, Peter Tiño, Xin Yao
2006	A	`conf`	Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. PPSN Peter Tiño
2006	—	`conf`	Does Money Matter? An Artificial Intelligence Approach. JCIS Jane M. Binner, Barry Jones, Graham Kendall, Jonathan A. Tepper, Peter Tiño
2006	J	`jnl`	Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Comput. Peter Tiño, Igor Farkas, Jort van Mourik
2006	J	`jnl`	How accurate are the time delay estimates in gravitational lensing? CoRR Juan Carlos Cuevas-Tello, Peter Tiño, Somak Raychaudhury
2006	J	`jnl`	Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. Neural Comput. Peter Tiño, Ashley J. S. Mills
2005	—	`conf`	Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. ICNC (2) Peter Tiño, Ashley J. S. Mills
2005	J	`jnl`	Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. Gavin Brown, Jeremy L. Wyatt, Peter Tiño
2005	—	`conf`	On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. ICNC (2) Peter Tiño, Igor Farkas
2005	C	`conf`	Recursive Self-organizing Map as a Contractive Iterative Function System. IDEAL Peter Tiño, Igor Farkas, Jort van Mourik
2005	J	`jnl`	Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization. IEEE Trans. Knowl. Data Eng. Ian T. Nabney, Yi Sun, Peter Tiño, Ata Kabán
2004	A*	`conf`	A generative probabilistic approach to visualizing sets of symbolic sequences. KDD Peter Tiño, Ata Kabán, Yi Sun
2004	A	`conf`	Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. PPSN Richard Price, Peter Tiño
2004	Misc	`conf`	Introducing a Star Topology into Latent Class Models for Collaborative Filtering. AIAI Gabriela Polcicová, Peter Tiño
2004	J	`jnl`	Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns. Neural Networks Gabriela Polcicová, Peter Tiño
2004	J	`jnl`	Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Networks Peter Tiño, Michal Cernanský, Lubica Benusková
2004	J	`jnl`	Nonlinear Prediction of Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Peter Tiño, Ian T. Nabney, Bruce S. Williams, Jens Lösel, Yi Sun
2004	—	`conf`	On Early Stages of Learning in Connectionist Models with Feedback Connections. AAAI Technical Report (3) Peter Tiño, Barbara Hammer
2004	A	`ed.`	Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings PPSN Xin Yao, Edmund K. Burke, José Antonio Lozano, Jim Smith, Juan Julián Merelo Guervós, John A. Bullinaria, Jonathan E. Rowe, Peter Tiño, Ata Kabán, Hans-Paul Schwefel
2003	J	`jnl`	Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Comput. Peter Tiño, Barbara Hammer
2003	J	`jnl`	Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Comput. Barbara Hammer, Peter Tiño
2002	C	`conf`	A General Framework for a Principled Hierarchical Visualization of Multivariate Data. IDEAL Ata Kabán, Peter Tiño, Mark A. Girolami
2002	C	`conf`	Architectural Bias in Recurrent Neural Networks - Fractal Analysis. ICANN Peter Tiño, Barbara Hammer
2002	J	`jnl`	Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way. IEEE Trans. Pattern Anal. Mach. Intell. Peter Tiño, Ian T. Nabney
2001	J	`jnl`	Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks). Neural Comput. Peter Tiño, Bill G. Horne, C. Lee Giles
2001	J	`jnl`	Financial volatility trading using recurrent neural networks. IEEE Trans. Neural Networks Peter Tiño, Christian Schittenkopf, Georg Dorffner
2001	J	`jnl`	Predicting the Future of Discrete Sequences from Fractal Representations of the Past. Mach. Learn. Peter Tiño, Georg Dorffner
2001	C	`conf`	Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds. ICANN Peter Tiño, Ian T. Nabney, Yi Sun
2001	J	`jnl`	Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series. Pattern Anal. Appl. Peter Tiño, Christian Schittenkopf, Georg Dorffner
2000	—	`conf`	Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition. IJCNN (2) Peter Tiño, Michal Stancík, Lubica Benusková
2000	—	`conf`	The profitability of trading volatility using real-valued and symbolic models. CIFEr Christian Schittenkopf, Peter Tiño, Georg Dorffner
1999	—	`conf`	Building Predictive Models from Fractal Representations of Symbolic Sequences. NIPS Peter Tiño, Georg Dorffner
1999	J	`jnl`	Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences. IEEE Trans. Neural Networks Peter Tiño, Miroslav Koteles
1999	—	`conf`	Graded Grammaticality in Prediction Fractal Machines. NIPS Shan Parfitt, Peter Tiño, Georg Dorffner
1999	J	`jnl`	Spatial representation of symbolic sequences through iterative function systems. IEEE Trans. Syst. Man Cybern. Part A Peter Tiño
1998	—	`conf`	Recurrent Neural Networks with Iterated Function Systems Dynamics. NC Peter Tiño, Georg Dorffner
1998	—	`conf`	Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics. Hybrid Neural Systems Peter Tiño, Georg Dorffner, Christian Schittenkopf
1997	—	`conf`	Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences. KES (2) Peter Tiño, V. Vojtek
1997	—	`conf`	Modeling Complex Symbolic Sequences with Neural Based Systems. ICANNGA Peter Tiño, V. Vojtek
1996	J	`jnl`	Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Networks Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles
1995	J	`jnl`	Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model. Neural Comput. Peter Tiño, Jozef Sajda
1995	—	`conf`	Learning long-term dependencies is not as difficult with NARX networks. NIPS Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles