Peter Tiño

Peter Tino: Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get? Entropy 15(4): 1202-1220 (2013)

Alessio Micheli, Frank-Michael Schleif, Peter Tino: Novel approaches in machine learning and computational intelligence. Neurocomputing 112: 1-3 (2013)

Peter Tino, Ali Rodan: Short term memory in input-driven linear dynamical systems. Neurocomputing 112: 58-63 (2013)

Orla M. Doyle, Krasimira Tsaneva-Atansaova, James Harte, Paul A. Tiffin, Peter Tino, Vanessa Díaz-Zuccarini: Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models. IEEE Trans. Biomed. Engineering 60(3): 735-742 (2013)

Peter Tino, Siang Yew Chong, Xin Yao: Complex Coevolutionary Dynamics - Structural Stability and Finite Population Effects. IEEE Trans. Evolutionary Computation 17(2): 155-164 (2013)

Shereen Fouad, Peter Tino, Somak Raychaudhury, Petra Schneider: Incorporating Privileged Information Through Metric Learning. IEEE Trans. Neural Netw. Learning Syst. 24(7): 1086-1098 (2013)

Boris Rudolf, Mária Markosová, Martin Cajági, Peter Tino: Degree distribution and scaling in the Connecting Nearest Neighbors model. CoRR abs/1304.3375 (2013)

Richard Price, Peter Tino, Georgios K. Theodoropoulos: Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks. MONET 17(3): 378-394 (2012)

Ali Rodan, Peter Tino: Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps. Neural Computation 24(7): 1822-1852 (2012)

Shereen Fouad, Peter Tino: Adaptive Metric Learning Vector Quantization for Ordinal Classification. Neural Computation 24(11): 2825-2851 (2012)

Siang Yew Chong, Peter Tino, Day Chyi Ku, Xin Yao: Improving Generalization Performance in Co-Evolutionary Learning. IEEE Trans. Evolutionary Computation 16(1): 70-85 (2012)

Shereen Fouad, Peter Tino, Somak Raychaudhury, Petra Schneider: Learning Using Privileged Information in Prototype Based Models. ICANN (2) 2012: 322-329

Shereen Fouad, Peter Tino: Prototype Based Modelling for Ordinal Classification. IDEAL 2012: 208-215

Peter Tino, Somak Raychaudhury: Computational Intelligence in Astronomy - A Win-Win Situation. TPNC 2012: 57-71

Huanhuan Chen, Peter Tino, Xin Yao, Ali Rodan: Learning in the Model Space for Fault Diagnosis. CoRR abs/1210.8291 (2012)

Peter Tiño, Hongya Zhao, Hong Yan: Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform. IEEE/ACM Trans. Comput. Biology Bioinform. 8(4): 1093-1107 (2011)

Ali Rodan, Peter Tino: Minimum Complexity Echo State Network. IEEE Transactions on Neural Networks 22(1): 131-144 (2011)

Ali Rodan, Peter Tiño: Negatively Correlated Echo State Networks. ESANN 2011

Nikolay I. Nikolaev, Peter Tino, Evgueni N. Smirnov: Time-Dependent Series Variance Estimation via Recurrent Neural Networks. ICANN (1) 2011: 176-184

Phil Weber, Behzad Bordbar, Peter Tino: Real-Time Detection of Process Change using Process Mining. ICCSW 2011: 108-114

Phil Weber, Behzad Bordbar, Peter Tino: A Principled Approach to the Analysis of Process Mining Algorithms. IDEAL 2011: 474-481

Peter Tino: One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression. ISNN (2) 2011: 37-46

Jakub Mazgut, Martina Paulinyová, Peter Tino: Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis. MEMICS 2011: 146-154

Weishan Dong, Tianshi Chen, Peter Tino, Xin Yao: Scaling Up Estimation of Distribution Algorithms For Continuous Optimization. CoRR abs/1111.2221 (2011)

Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury, Xin Yao, Markus Harva: Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application. Pattern Recognition 43(3): 1165-1179 (2010)

Jakub Mazgut, Peter Tino, Mikael Bodén, Hong Yan: Multilinear Decomposition and Topographic Mapping of Binary Tensors. ICANN (1) 2010: 317-326

Ali Rodan, Peter Tino: Simple Deterministically Constructed Recurrent Neural Networks. IDEAL 2010: 267-274

Richard Price, Peter Tiño: Adapting to NAT timeout values in P2P overlay networks. IPDPS Workshops 2010: 1-6

Colin Fyfe, Peter Tiño, Darryl Charles, Cesar García-Osorio, Hujun Yin (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2010, 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Lecture Notes in Computer Science 6283, Springer 2010, isbn 978-3-642-15380-8

Peter Tino: Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. BMC Bioinformatics 10: 310 (2009)

Siang Yew Chong, Peter Tino, Xin Yao: Relationship Between Generalization and Diversity in Coevolutionary Learning. IEEE Trans. Comput. Intellig. and AI in Games 1(3): 214-232 (2009)

Huanhuan Chen, Peter Tiño, Xin Yao: Predictive Ensemble Pruning by Expectation Propagation. IEEE Trans. Knowl. Data Eng. 21(7): 999-1013 (2009)

Huanhuan Chen, Peter Tino, Xin Yao: Probabilistic Classification Vector Machines. IEEE Transactions on Neural Networks 20(6): 901-914 (2009)

Richard Price, Peter Tiño: Still alive: Extending keep-alive intervals in P2P overlay networks. CollaborateCom 2009: 1-10

Nikolaos Gianniotis, Peter Tiño: Visualization of Structured Data via Generative Probabilistic Modeling. Similarity-Based Clustering 2009: 118-137

Nikolaos Gianniotis, Peter Tino, Steve Spreckley, Somak Raychaudhury: Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. ICANN (1) 2009: 567-576

Peter Tino, Hongya Zhao, Hong Yan: Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data. IJCBS 2009: 48-51

Xiaoxia Wang, Peter Tiño, Mark A. Fardal, Somak Raychaudhury, Arif Babul: Fast parzen window density estimator. IJCNN 2009: 3267-3274

Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury, Xin Yao, Markus Harva: Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application. CoRR abs/0908.3706 (2009)

Siang Yew Chong, Peter Tino, Xin Yao: Measuring Generalization Performance in Coevolutionary Learning. IEEE Trans. Evolutionary Computation 12(4): 479-505 (2008)

Nikolaos Gianniotis, Peter Tiño: Visualization of Tree-Structured Data Through Generative Topographic Mapping. IEEE Transactions on Neural Networks 19(8): 1468-1493 (2008)

Peter Tino: Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Recurrent Neural Networks 2008

Michal Cernanský, Peter Tiño: Predictive Modeling with Echo State Networks. ICANN (1) 2008: 778-787

Xiaoxia Wang, Peter Tiño, Mark A. Fardal: Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model. ECML/PKDD (2) 2008: 566-581

Peter Tino: Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Neural Computation 19(4): 1056-1081 (2007)

Nikolaos Gianniotis, Peter Tino: Visualisation of tree-structured data through generative probabilistic modelling. ESANN 2007: 97-102

Michal Cernanský, Peter Tino: Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences. ICANN (1) 2007: 618-627

Peter Tiño: Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks. ICONIP (1) 2007: 405-414

Peter Tino, Nikolaos Gianniotis: Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. IJCAI 2007: 1083-1088

Peter Tino: On Conditions for Intermittent Search in Self-organizing Neural Networks. MICAI 2007: 172-181

Peter Tino, Barbara Hammer, Mikael Bodén: Markovian Bias of Neural-based Architectures With Feedback Connections. Perspectives of Neural-Symbolic Integration 2007: 95-133

Hujun Yin, Peter Tiño, Emilio Corchado, William Byrne, Xin Yao (Eds.): Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings. Lecture Notes in Computer Science 4881, Springer 2007, isbn 978-3-540-77225-5

Peter Tiño, Ashley J. S. Mills: Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. Neural Computation 18(3): 591-613 (2006)

Peter Tiño, Igor Farkas, Jort van Mourik: Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Computation 18(10): 2529-2567 (2006)

Juan C. Cuevas-Tello, Peter Tiño, Somak Raychaudhury: A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses. ECML 2006: 614-621

Huanhuan Chen, Peter Tiño, Xin Yao: A Probabilistic Ensemble Pruning Algorithm. ICDM Workshops 2006: 878-882

Jane M. Binner, Barry Jones, Graham Kendall, Jonathan A. Tepper, Peter Tiño: Does Money Matter? An Artificial Intelligence Approach. JCIS 2006

Peter Tino: Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. PPSN 2006: 633-640

Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury: How accurate are the time delay estimates in gravitational lensing? CoRR abs/astro-ph/0605042 (2006)

Gavin Brown, Jeremy L. Wyatt, Peter Tino: Managing Diversity in Regression Ensembles. Journal of Machine Learning Research 6: 1621-1650 (2005)

Ian T. Nabney, Yi Sun, Peter Tiño, Ata Kabán: Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization. IEEE Trans. Knowl. Data Eng. 17(3): 384-400 (2005)

Peter Tiño, Ashley J. S. Mills: Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. ICNC (2) 2005: 666-675

Peter Tiño, Igor Farkas: On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. ICNC (2) 2005: 676-685

Peter Tiño, Igor Farkas, Jort van Mourik: Recursive Self-organizing Map as a Contractive Iterative Function System. IDEAL 2005: 327-334

Peter Tiño, Ian T. Nabney, Bruce S. Williams, Jens Lösel, Yi Sun: Nonlinear Prediction of Quantitative Structure-Activity Relationships. Journal of Chemical Information and Modeling 44(5): 1647-1653 (2004)

Gabriela Polcicová, Peter Tiño: Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns. Neural Networks 17(8-9): 1183-1199 (2004)

Peter Tino, Michal Cernanský, Lubica Benusková: Markovian architectural bias of recurrent neural networks. IEEE Transactions on Neural Networks 15(1): 6-15 (2004)

Gabriela Polcicova, Peter Tiño: Introducing a Star Topology into Latent Class Models for Collaborative Filtering. AIAI 2004: 293-304

Peter Tiño, Ata Kabán, Yi Sun: A generative probabilistic approach to visualizing sets of symbolic sequences. KDD 2004: 701-706

Richard Price, Peter Tiño: Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. PPSN 2004: 982-990

Xin Yao, Edmund K. Burke, José Antonio Lozano, Jim Smith, Juan J. Merelo Guervós, John A. Bullinaria, Jonathan E. Rowe, Peter Tiño, Ata Kabán, Hans-Paul Schwefel (Eds.): Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings. Lecture Notes in Computer Science 3242, Springer 2004, isbn 3-540-23092-0

Barbara Hammer, Peter Tiño: Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation 15(8): 1897-1929 (2003)

Peter Tiño, Barbara Hammer: Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation 15(8): 1931-1957 (2003)

Peter Tiño, Ian T. Nabney: Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way. IEEE Trans. Pattern Anal. Mach. Intell. 24(5): 639-656 (2002)

Peter Tiño, Barbara Hammer: Architectural Bias in Recurrent Neural Networks - Fractal Analysis. ICANN 2002: 1359-1364

Ata Kabán, Peter Tiño, Mark Girolami: A General Framework for a Principled Hierarchical Visualization of Multivariate Data. IDEAL 2002: 518-523

Peter Tiño, Georg Dorffner: Predicting the Future of Discrete Sequences from Fractal Representations of the Past. Machine Learning 45(2): 187-217 (2001)

Peter Tiño, Bill G. Horne, C. Lee Giles: Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks). Neural Computation 13(6): 1379-1414 (2001)

Peter Tiño, Christian Schittenkopf, Georg Dorffner: Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series. Pattern Anal. Appl. 4(4): 283-299 (2001)

Peter Tiño, Christian Schittenkopf, Georg Dorffner: Financial volatility trading using recurrent neural networks. IEEE Transactions on Neural Networks 12(4): 865-874 (2001)

Peter Tiño, Ian T. Nabney, Yi Sun: Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds. ICANN 2001: 421-428

Peter Tiño, Michal Stancík, Lubica Benusková: Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition. IJCNN (2) 2000: 265-270

Peter Tino, Miroslav Koteles: Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences. IEEE Transactions on Neural Networks 10(2): 284-302 (1999)

Peter Tiño: Spatial representation of symbolic sequences through iterative function systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A 29(4): 386-393 (1999)

Shan Parfitt, Peter Tiño, Georg Dorffner: Graded Grammaticality in Prediction Fractal Machines. NIPS 1999: 52-58

Peter Tiño, Georg Dorffner: Building Predictive Models from Fractal Representations of Symbolic Sequences. NIPS 1999: 645-651

Peter Tiño, Georg Dorffner, Christian Schittenkopf: Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics. Hybrid Neural Systems 1998: 255-269

Peter Tiño, Georg Dorffner: Recurrent Neural Networks with Iterated Function Systems Dynamics. NC 1998: 526-532

Peter Tino, V. Vojtek: Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences. KES (2) 1997: 551-558

Tsungnan Lin, Bill G. Horne, Peter Tino, C. Lee Giles: Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Netw. Learning Syst. 7(6): 1329-1338 (1996)

Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles: Learning long-term dependencies is not as difficult with NARX networks. NIPS 1995: 577-583