0003	Recognizing Evoked Potentials in a Virtual Environment, Jessica D. Bayliss and Dana H. Ballard
0010	A Neurodynamical Approach to Visual Attention, Gustavo Deco and Josef Zihl
0017	Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information, Thea B. Ghiselli-Crippa and Paul W. Munro
0024	Acquisition in Autoshaping, Sham Kakade and Peter Dayan
0031	Robust Recognition of Noisy and Superimposed Patterns via Selective Attention, Soo-Young Lee and Michael C. Mozer
0038	Perceptual Organization Based on Temporal Dynamics, Xiuwen Liu and DeLiang L. Wang
0045	Information Factorization in Connectionist Models of Perception, Javier R. Movellan and James L. McClelland
0052	Graded Grammaticality in Prediction Fractal Machines, Shan Parfitt, Peter Tino and Georg Dorffner
0059	Rules and Similarity in Concept Learning, Joshua B. Tenenbaum
0066	Evolving Learnable Languages, Bradley Tonkes, Alan Blair and Janet Wiles
0073	Learning Statistically Neutral Tasks without Expert Guidance, Ton Weijters, Antal van den Bosch and Eric Postma

Part II Neuroscience

0080	A Generative Model for Attractor Dynamics, Richard S. Zemel and Michael C. Mozer
0089	Recurrent Cortical Competition: Strengthen or Weaken?, Peter Adorjan, Lars Schwabe, Christian Piepenbrock and Klaus Obermayer
0096	Effective Learning Requires Neuronal Remodeling of Hebbian Synapses, Gal Chechik, Isaac Meilijson and Eytan Ruppin
0103	Wiring Optimization in the Brain, Dmitri B. Chklovskii and Charles F. Stevens
0108	Optimal Sizes of Dendritic and Axonal Arbors, Dmitri B. Chklovskii
0115	Neural Representation of Multi-Dimensional Stimuli, Christian W. Eurich, Stefan D. Wilke and Helmut Schwegler
0122	Spiking Boltzmann Machines, Geoffrey E. Hinton and Andrew D. Brown
0129	Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly, David Horn, Nir Levy, Isaac Meilijson and Eytan Ruppin
0136	Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects?, Zhaoping Li
0143	Channel Noise in Excitable Neural Membranes, Amit Manwani, Peter N. Steinmetz and Christof Koch
0150	LTD Facilitates Learning in a Noisy Environment, Paul W. Munro and Gerardina Hernandez
0157	Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration, Panayiota Poirazi and Bartlett W. Mel
0164	Predictive Sequence Learning in Recurrent Neocortical Circuits, Rajesh P. N. Rao and Terrence J. Sejnowski
0171	A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks, Alfonso Renart, Nestor Parga and Edmund T. Rolls
0178	Information Capacity and Robustness of Stochastic Neuron Models, Elad Schneidman, Idan Segev and Naftali Tishby
0185	An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task, Akaysha C. Tang, Barak A. Pearlmutter, Tim A. Hely, Michael Zibulevsky and Michael P. Weisend
0192	Population Decoding Based on an Unfaithful Model, Si Wu, Hiroyuki Nakahara, Noboru Murata and Shun-ichi Amari

Part III Theory

0199	Spike-based Learning Rules and Stabilization of Persistent Neural Activity, Xiaohui Xie and H. Sebastian Seung
0209	A Variational Baysian Framework for Graphical Models, Hagai Attias
0216	Model Selection in Clustering by Uniform Convergence Bounds, Joachim M. Buhmann and Marcus Held
0223	Uniqueness of the SVM Solution, Christopher J. C. Burges and David J. Crisp
0230	Model Selection for Support Vector Machines, Olivier Chapelle and Vladimir N. Vapnik
0237	Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers, A. C. C. Coolen and C. W. H. Mace
0244	A Geometric Interpretation of v-SVM Classifiers, David J. Crisp and Christopher J. C. Burges
0251	Efficient Approaches to Gaussian Process Classification, Lehel Csato, Ernest Fokoue, Manfred Opper, Bernhard Schottky and Ole Winther
0258	Potential Boosters?, Nigel Duffy and David Helmbold
0265	Bayesian Averaging is Well-Temperated, Lars Kai Hansen
0272	Regular and Irregular Gallager-zype Error-Correcting Codes, Yoshiyuki Kabashima, Tatsuto Murayama, David Saad and Renato Vicente
0279	Mixture Density Estimation, Jonathan Q. Li and Andrew R. Barron
0286	Statistical Dynamics of Batch Learning, Song Li and K. Y. Michael Wong
0293	Neural Computation with Winner-Take-All as the Only Nonlinear Operation, Wolfgang Maass
0300	Boosting with Multi-Way Branching in Decision Trees, Yishay Mansour and David McAllester
0307	Inference for the Generalization Error Claude Nadeau and Yoshua Bengio
0314	Resonance in a Stochastic Neuron Model with Delayed Interaction, Toru Ohira, Yuzuru Sato and Jack D. Cowan
0321	Understanding Stepwise Generalization of Support Vector Machines: a Toy Model, Sebastian Risau-Gusman and Mirta B. Gordon
0328	Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks, Michael Schmitt
0335	Noisy Neural Networks and Generalizations, Hava T. Siegelmann, Alexander Roitershtein and Asa Ben-Hur
0342	The Entropy Regularization Information Criterion, Alexander J. Smola, John Shawe-Taylor, Bernhard Scholkopf and Robert C. Williamson
0349	Probabilistic Methods for Support Vector Machines, Peter Sollich
0356	Algebraic Analysis for Non-regular Learning Machines, Sumio Watanabe
0363	Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems, L. Q. Zhang, Shun-ichi Amari and A. Cichocki

Part IV Algorithms and Architecture

0370	Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions, Tong Zhang
0379	Robust Full Bayesian Methods for Neural Networks, Christophe Andrieu, Joao F. G. de Freitas and Arnaud Doucet
0386	Independent Factor Analysis with Temporally Structured Sources, Hagai Attias
0393	Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks, David Barber and Peter Sollich
0400	Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks, Yoshua Bengio and Samy Bengio
0407	Robust Neural Network Regression for Offline and Online Learning, Thomas Briegel and Volker Tresp
0414	Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints, Miguel A. Carreira-Perpinan
0421	Transductive Inference for Estimating Values of Functions, Olivier Chapelle, Vladimir N. Vapnik and Jason Weston
0428	The Nonnegative Boltzmann Machine, Oliver B. Downs, David J.C. MacKay and Daniel D. Lee
0435	Differentiating Functions of the Jacobian with Respect to the Weights, Gary William Flake and Barak A. Pearlmutter
0442	Local Probability Propagation for Factor Analysis, Brendan J. Frey
0449	Variational Inference for Bayesian Mixtures of Factor Analysers, Zoubin Ghahramani and Matthew J. Beal
0456	Bayesian Transduction, Thore Graepel, Ralf Herbrich and Klaus Obermayer
0463	Learning to Parse Images, Geoffrey E. Hinton, Zoubin Ghahramani and Yee Whye Teh
0470	Maximum Entropy Discrimination, Tommi Jaakkola, Marina Meila and Tony Jebara
0477	Topographic Transformation as a Discrete Latent Variable, Nebojsa Jojic and Brendan J. Frey
0484	An Improved Decomposition Algorithm for Regression Support Vector Machines, Pavel Laskov
0491	Algorithms for Independent Components Analysis and Higher Order Statistics, Daniel D. Lee, Uri Rokni and Haim Sompolinsky
0498	The Relaxed Online Maximum Margin Algorithm, Yi Li and Philip M. Long
0505	Bayesian Network Induction via Local Neighborhoods, Dimitris Margaritis and Sebastian Thrun
0512	Boosting Algorithms as Gradient Descent, Liew Mason, Jonathan Baxter, Peter Bartlett and Marcus Frean
0519	A Multi-class Linear Learning Algorithm Related to Winnow, Chris Mesterharm
0526	Invariant Feature Extraction and Classification in Kernel Spaces, Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Scholkopf, Alexander J. Smola and Klaus-Robert Muller
0533	Approximate Inference A lgorithms for Two-Layer Bayesian Networks, Andrew Y. Ng and Michael I. Jordan
0540	Optimal Kernel Shapes for Local Linear Regression, Dirk Ormoneit and Trevor Hastie
0547	Large Margin DAGs for Multiclass Classification, John C. Platt, Nello Cristianini and John Shawe-Taylor
0554	The Infinite Gaussian Mixture Model, Carl Edward Rasmussen
0561	v-Arc: Ensemble Learning in the Presence of Outliers, Gunnar Rätsch, Bernhard Scholkopf, Alexander J. Smola, Klaus-Robert Muller, Takashi Onoda and Sebastian Mika
0568	Nonlinear Discriminant Analysis Using Kernel Functions, Volker Roth and Volker Steinhage
0575	An Analysis of Turbo Decoding with Gaussian Densities, Paat Rusmevichientong and Benjamin Van Roy
0582	Support Vector Method for Novelty Detection, Bernhard Scholkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor and John C. Platt
0589	Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks, Mike Schuster
0596	Greedy Importance Sampling, Dale Schuurmans
0603	Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers, Matthias Seeger
0610	Leveraged Vector Machines, Yoram Singer
0617	Agglomerative Information Bottleneck, Noam Slonim and Naftali Tishby
0624	Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks, Masashi Sugiyama and Hidemitsu Ogawa
0631	Predictive App roaches for Choosing Hyperparameters in Gaussian Processes, S. Sundararajan and S. Sathiya Keerthi
0638	On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling, Peter Sykacek
0645	Building Predictive Models from Fractal Representations of Symbolic Sequences, Peter Tino and Georg Dorffner
0652	The Relevance Vector Machine, Michael E. Tipping
0659	Support Vector Method for Multivariate Density Estimation, Vladimir N. Vapnik and Sayan Mukherjee
0666	Dual Estimation and the Unscented Transformation, Eric A. Wan, Rudolph van der Merwe and Alex T. Nelson
0673	Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology, Yair Weiss and William T. Freeman
0680	A MCMC Approach to Hierarchical Mixture Modelling, Christopher K. I. Williams
0687	Data Visualization and Feature Selection: New Algorithms for Nongaussian Data, Howard Hua Yang and John Moody

Part V Implementation

0694	Manifold Stochastic Dynamics for Bayesian Learning, Mark Ziochin and Yoram Baram
0703	The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning, Charles Lee Isbell, Jr. and Parry Husbands
0710	An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control, Oliver Landolt and Steve Gyger
0717	A Winner-Take-All Circuit with Controllable Soft Max Property, Shih-Chii Liu.
0724	A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion, Girish N. Patel, Edgar A. Brown and Stephen P. DeWeerth
0731	Bifurcation Analysis of a Silicon Neuron, Girish N. Patel, Gennady S. Cymbalyuk, Ronald L. Calabrese and Stephen P. DeWeerth

Part VI Speech, Handwriting and Signal Processing

0738	An Analog VLSI Model of Periodicity Extraction, Andre van Schaik
0747	An Oscillatory Correlation Frame work for Computational Auditory Scene Analysis, Guy J. Brown and DeLiang L. Wang
0754	Bayesian Modelling of fMRI lime Series, Pedro A. d. F. R. Hojen-Sørensen, Lars Kai Hansen and Carl Edward Rasmussen
0761	Neural System Model of Human Sound Localization, Craig T. Jin and Simon Carlile
0768	Spectral Cues in Human Sound Localization, Craig T. Jin, Anna Corderoy, Simon Carlile and Andre van Schaik
0775	Broadband Direction-Of-Arrival Estimation Based on Second Order Statistics, Justinian Rosca, Joseph O Ruanaidh, Alexander Jourjine and Scott Rickard
0782	Constrained Hidden Markov Models, Sam Roweis
0789	Online Independent Component Analysis with Local Learning Rate Adaptation, Nicol N. Schraudolph and Xavier Giannakopoulos
0796	Speech Modelling Using Subspace and EM Techniques, Gavin Smith, Joao F. G. de Freitas, Tony Robinson and Mahesan Niranjan

Part VII Visual Processing

0803	Search for Information Bearing Components in Speech, Howard Hua Yang and Hynek Hermansky
0813	Audio Vision: Using Audio-Visual Synchrony to Locate Sounds, John Hershey and Javier R. Movellan
0820	Bayesian Reconstruction of 3D Human Motion from Single-Camera Video, Nicholas R. Howe, Michael E. Leventon and William T. Freeman
0827	Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA, Aapo Hyvarinen and Patrik Hoyer
0834	An Information-Theoretic Framework for Understanding Saccadic Eye Movements, Tai Sing Lee and Stella X. Yu
0841	Learning Sparse Codes with a Mixture-of-Gaussians Prior, Bruno A. Olshausen and K. Jarrod Millman
0848	Hierarchical Image Probability (H1P) Models, Clay D. Spence and Lucas Parra
0855	Scale Mixtures of Gaussians and the Statistics of Natural Images, Martin J. Wainwright and Eero P. Simoncelli
0862	A SNoW-Based Face Detector, Ming-Hsuan Yang, Dan Roth and Narendra Ahuja

Part VIII Applications

0869	Managing Uncertainty in Cue Combination, Zhiyong Yang and Richard S. Zemel
0879	Robust Learning of Chaotic Attractors, Rembrandt Bakker, Jaap C. Schouten, Marc-Olivier Coppens, Floris Takens, C. Lee Giles and Cor M. van den Bleek
0886	Image Representations for Facial Expression Coding, Marian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman and Terrence J. Sejnowski
0893	Low Power Wireless Communication via Reinforcement Learning, Timothy X. Brown
0900	Learning Informative Statistics: A Nonparametnic Approach, John W. Fisher III, Alexander T. Ihier and Paul A. Viola
0907	Kirchoff Law Markov Fields for Analog Circuit Design, Richard M. Golden
0914	Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization, Thomas Hofmann
0921	Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting, Yuansong Liao and John Moody
0928	From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data, Eric Mjolsness, Tobias Mann, Rebecca Castano and Barbara Wold
0935	Churn Reduction in the Wireless Industry, Michael C. Mozer, Richard Wolniewicz, David B. Grimes, Eric Johnson and Howard Kaushansky
0942	Unmixing Hyperspectral Data, Lucas Parra, Clay D. Spence, Paul Sajda, Andreas Ziehe and Klaus-Robert Muller
0949	Application of Blind Separation of Sources to Optical Recording of Brain Activity, Holger Schoner, Martin Stetter, Ingo SchieBi, John E.W. Mayhew, Jennifer Lund, Niall McLoughlin and Klaus Obermayer
0956	Reinforcement Learning for Spoken Dialogue Systems, Satinder Singh, Michael Kearns, Diane Litman and Marilyn Walker
0963	Image Recognition in Context: Application to Microscopic Urinalysis, Xubo B. Song, Joseph Sill, Yaser Abu-Mostafa and Harvey Kasdan
0970	Generalized Model Selection for Unsupervised Learning in High Dimensions, Shivakumar Vaithyanathan and Byron Dom

Part IX Control, Navigation and Planning

0977	Learning from User Feedback in Image Retrieval Systems, Nuno Vasconcelos and Andrew Lippman
0987	An Environment Model for Nonstationary Reinforcement Learning, Samuel P. M. Choi, Dit-Yan Yeung and Nevin L. Zhang
0994	State Abstraction in MAXQ Hierarchical Reinforcement Learning, Thomas G. Dietterich
1001	Approximate Planning in Large POMDPs via Reusable Trajectories, Michael Kearns, Yishay Mansour and Andrew Y. Ng
1008	Actor-Critic Algorithms, Vijay R. Konda and John N. Tsitsiklis
1015	Bayesian Map Learning in Dynamic Environments, Kevin P. Murphy
1022	Policy Search via Density Estimation, Andrew Y. Ng, Ronald Parr and Daphne Koller
1029	Neural Network Based Model Predictive Control, Stephen Piche, Jim Keeler, Greg Martin, Gene Boe, Doug Johnson and Mark Gerules
1036	Reinforcement Learning Using Approximate Belief States, Andrés Rodriguez, Ronald Parr and Daphne Koller
1043	Coastal Navigation with Mobile Robots, Nicholas Roy and Sebastian Thrun
1050	Learning Factored Representations for Partially Observable Markov Decision Processes, Brian Sallans
1057	Policy Gradient Methods for Reinforcement Learning with Function Approximation, Richard S. Sutton, David McAllester, Satinder Singh and Yishay Mansour
1064	Monte Carlo POMDPs, Sebastian Thrun
1071	Index of Authors
1075	Keyword Index