Peter Tiño

Peter Tino

	2009
51		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
50		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)
49		Huanhuan Chen, Peter Tiño, Xin Yao: Predictive Ensemble Pruning by Expectation Propagation. IEEE Trans. Knowl. Data Eng. 21(7): 999-1013 (2009)
	2008
48		Xiaoxia Wang, Peter Tiño, Mark A. Fardal: Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model. ECML/PKDD (2) 2008: 566-581
47		Michal Cernanský, Peter Tiño: Predictive Modeling with Echo State Networks. ICANN (1) 2008: 778-787
46		Peter Tino: Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Recurrent Neural Networks 2008
45		Siang Yew Chong, Peter Tino, Xin Yao: Measuring Generalization Performance in Coevolutionary Learning. IEEE Trans. Evolutionary Computation 12(4): 479-505 (2008)
44		Nikolaos Gianniotis, Peter Tiño: Visualization of Tree-Structured Data Through Generative Topographic Mapping. IEEE Transactions on Neural Networks 19(8): 1468-1493 (2008)
	2007
43		Hujun Yin, Peter Tiño, Emilio Corchado, William Byrne, Xin Yao: Intelligent Data Engineering and Automated Learning - IDEAL 2007, 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings Springer 2007
42		Nikolaos Gianniotis, Peter Tino: Visualisation of tree-structured data through generative probabilistic modelling. ESANN 2007: 97-102
41		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
40		Peter Tiño: Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks. ICONIP (1) 2007: 405-414
39		Peter Tino, Nikolaos Gianniotis: Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. IJCAI 2007: 1083-1088
38		Peter Tino: On Conditions for Intermittent Search in Self-organizing Neural Networks. MICAI 2007: 172-181
37		Peter Tino, Barbara Hammer, Mikael Bodén: Markovian Bias of Neural-based Architectures With Feedback Connections. Perspectives of Neural-Symbolic Integration 2007: 95-133
36		Peter Tino: Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. Neural Computation 19(4): 1056-1081 (2007)
	2006
35		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
34		Huanhuan Chen, Peter Tiño, Xin Yao: A Probabilistic Ensemble Pruning Algorithm. ICDM Workshops 2006: 878-882
33		Jane M. Binner, Barry Jones, Graham Kendall, Jonathan A. Tepper, Peter Tiño: Does Money Matter? An Artificial Intelligence Approach. JCIS 2006
32		Peter Tino: Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. PPSN 2006: 633-640
31		Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury: How accurate are the time delay estimates in gravitational lensing? CoRR abs/astro-ph/0605042: (2006)
30		Peter Tiño, Igor Farkas, Jort van Mourik: Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Computation 18(10): 2529-2567 (2006)
29		Peter Tiño, Ashley J. S. Mills: Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. Neural Computation 18(3): 591-613 (2006)
	2005
28		Peter Tiño, Ashley J. S. Mills: Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. ICNC (2) 2005: 666-675
27		Peter Tiño, Igor Farkas: On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. ICNC (2) 2005: 676-685
26		Peter Tiño, Igor Farkas, Jort van Mourik: Recursive Self-organizing Map as a Contractive Iterative Function System. IDEAL 2005: 327-334
25		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)
24		Gavin Brown, Jeremy L. Wyatt, Peter Tino: Managing Diversity in Regression Ensembles. Journal of Machine Learning Research 6: 1621-1650 (2005)
	2004
23		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: Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings Springer 2004
22		Gabriela Polcicova, Peter Tiño: Introducing a Star Topology into Latent Class Models for Collaborative Filtering. AIAI 2004: 293-304
21		Peter Tiño, Ata Kabán, Yi Sun: A generative probabilistic approach to visualizing sets of symbolic sequences. KDD 2004: 701-706
20		Richard Price, Peter Tiño: Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. PPSN 2004: 982-990
19		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)
18		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)
	2003
17		Barbara Hammer, Peter Tiño: Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. Neural Computation 15(8): 1897-1929 (2003)
16		Peter Tiño, Barbara Hammer: Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation 15(8): 1931-1957 (2003)
	2002
15		Peter Tiño, Barbara Hammer: Architectural Bias in Recurrent Neural Networks - Fractal Analysis. ICANN 2002: 1359-1364
14		Ata Kabán, Peter Tiño, Mark Girolami: A General Framework for a Principled Hierarchical Visualization of Multivariate Data. IDEAL 2002: 518-523
13		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)
	2001
12		Peter Tiño, Ian T. Nabney, Yi Sun: Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds. ICANN 2001: 421-428
11		Peter Tiño, Georg Dorffner: Predicting the Future of Discrete Sequences from Fractal Representations of the Past. Machine Learning 45(2): 187-217 (2001)
10		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)
9		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)
	2000
8		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
	1999
7		Shan Parfitt, Peter Tiño, Georg Dorffner: Graded Grammaticality in Prediction Fractal Machines. NIPS 1999: 52-58
6		Peter Tiño, Georg Dorffner: Building Predictive Models from Fractal Representations of Symbolic Sequences. NIPS 1999: 645-651
5		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)
4		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)
	1998
3		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
2		Peter Tiño, Georg Dorffner: Recurrent Neural Networks with Iterated Function Systems Dynamics. NC 1998: 526-532
	1995
1		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

Coauthor Index

1	Lubica Benusková (Lubica Benuskova)	[8]
2	Jane M. Binner	[33]
3	Mikael Bodén	[37]
4	Gavin Brown	[24]
5	John A. Bullinaria	[23]
6	Edmund K. Burke	[23]
7	William J. Byrne (William Byrne)	[43]
8	Michal Cernanský	[41] [47]
9	Huanhuan Chen	[34] [49]
10	Siang Yew Chong	[45]
11	Emilio Corchado	[43]
12	Juan C. Cuevas-Tello	[31] [35] [50]
13	Georg Dorffner	[2] [3] [6] [7] [9] [11]
14	Mark A. Fardal	[48]
15	Igor Farkas	[26] [27] [30]
16	Nikolaos Gianniotis	[39] [42] [44] [51]
17	C. Lee Giles (Clyde Lee Giles)	[1] [10]
18	Mark A. Girolami (Mark Girolami)	[14]
19	Juan Julián Merelo Guervós (Juan J. Merelo Guervós)	[23]
20	Barbara Hammer	[15] [16] [17] [37]
21	Markus Harva	[50]
22	Bill G. Horne	[1] [10]
23	Barry Jones	[33]
24	Ata Kabán (Ata Kaban)	[14] [21] [23] [25]
25	Graham Kendall	[33]
26	Gabriela Kosková (Gabriela Polcicová, Gabriela Polcicova)	[18] [22]
27	Miroslav Koteles	[5]
28	Tsungnan Lin (Tsung-Nan Lin)	[1]
29	Jens Lösel	[19]
30	José Antonio Lozano (Jose Antonio Lozano)	[23]
31	Ashley J. S. Mills	[28] [29]
32	Jort van Mourik	[26] [30]
33	Ian T. Nabney	[12] [13] [19] [25]
34	Shan Parfitt	[7]
35	Richard Price	[20]
36	Somak Raychaudhury	[31] [35] [50] [51]
37	Jonathan E. Rowe	[23]
38	Christian Schittenkopf	[3] [9]
39	Hans-Paul Schwefel	[23]
40	Jim Smith	[23]
41	Steve Spreckley	[51]
42	Michal Stancík	[8]
43	Yi Sun	[12] [19] [21] [25]
44	Jonathan A. Tepper	[33]
45	Xiaoxia Wang	[48]
46	Bruce S. Williams	[19]
47	Jeremy L. Wyatt	[24]
48	Xin Yao	[23] [34] [43] [45] [49] [50]
49	Hujun Yin	[43]

Colors in the list of coauthors