0003	Evidence for a Forward Dynamics Model in Human Adaptive Motor Control, Nikhil Bhushan and Reza Shadmehr
0010	Perceiving without Learning: From Spirals to Inside/Outside Relations, Ke Chen and DeLiang L. Wang
0017	A Model for Associative Multiplication, G. Bjorn Christianson and Suzanna Becker
0024	Facial Memory Is Kernel Density Estimation (Almost), Matthew N. Dailey, Garrison W. Cottrell and Thomas A. Busey
0031	Multiple Paired Forward-Inverse Models for Human Motor Learning and Control, Masahiko Haruno, Daniel M. Wolpert and Mitsuo Kawato
0038	Utilizing lime: Asynchronous Binding, Bradley C. Love
0045	Mechanisms of Generalization in Perceptual Learning, Zili Liu and Daphna Weinshall
0052	A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes, Michael C. Mozer

Part II Neuroscience

0059	Bayesian Modeling of Human Concept Learning, Joshua B. Tenenbaum
0069	Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability, L. F. Abbott and Sen Song
0076	Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability, Peter Adorjan and Klaus Obermayer
0083	Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements?, Pierre Baraduc, Emmanuel Guigon and Yves Burnod
0090	Recurrent Cortical Amplification Produces Complex Cell Responses, Frances S. Chance, Sacha B. Nelson and L. F. Abbott
0097	Neuronal Regulation Implements Efficient Synaptic Pruning, Gal Chechik, Isaac Meilijson and Eytan Ruppin
0104	Divisive Normalization, Line Attractor Networks and Ideal Observers, Sophie Deneve, Alexandre Pouget and Peter E. Latham
0111	Synergy and Redundancy among Brain Cells of Behaving Monkeys, Itay Gat and Naftali Tishby
0118	Analyzing and Visualizing Single-Trial Event-Related Potentials, Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne and Terrence J. Sejnowski
0125	Spike-Based Compared to Rate-Based Hebbian Learning, Richard Kempter, Wuifram Gerstner and J. Leo van Hemmen
0132	Signal Detection in Noisy Weakly-Active Dendrites, Amit Manwani and Christof Koch
0139	The Role of Lateral Cortical Competition in Ocular Dominance Development, Christian Piepenbrock and Klaus Obermayer
0146	Multi-Electrode Spike Sorting by Clustering Transfer Functions, Dmitry Rinberg, Hanan Davidowitz and Naftali Tishby
0153	Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model, Eero P. Simoncelli and Odelia Schwartz
0160	Information Maximization in Single Neurons, Martin Stemmler and Christof Koch
0167	The Effect of Correlations on the Fisher Information of Population Codes, Hyoungsoo Yoon and Haim Sompolinsky

Part III Theory

0174	Distributional Population Codes and Multiple Motion Models, Richard S. Zemel and Peter Dayan
0183	Tractable Variational Structures for Approximating Graphical Models, David Barber and Wim Wiegerinck
0190	Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks, Peter L. Bartlett, Vitaly Maiorov and Ron Meir
0197	Dynamics of Supervised Learning with Restricted Training Sets, A. C. C. Coolen and David Saad
0204	Dynamically Adapting Kernels in Support Vector Machines, Nello Cristianini, Cohn Campbell and John Shawe-Taylor
0211	Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks, A. During, A. C. C. Coolen and D. Sherrington
0218	Finite-Dimensional Approximation of Gaussian Processes, Giancarlo Ferrari-Trecate, Christopher K. I. Williams and Manfred Opper
0225	Linear Hinge Loss and Average Margin, Claudio Gentile and Manfred K. Warmuth
0232	Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations, Didier Herschkowitz and Jean-Pierre Nadal
0239	Convergence of the Wake-Sleep Algorithm, Shiro Ikeda, Shun-ichi Amari and Hiroyuki Nakahara
0246	The Belief in TAP, Yoshiyuki Kabashima and David Saad
0253	Optimizing Classifers for Imbalanced Training Sets, Grigoris Karakoulas and John Shawe-Taylor
0260	Inference in Multilayer Networks via Large Deviation Bounds, Michael Kearns and Lawrence Saul
0267	Stationarity and Stability of Autoregressive Neural Network Processes, Friedrich Leisch, Adrian Trapletti and Kurt Hornik
0274	Computational Differences between Asymmetrical and Symmetrical Networks, Zhaoping Li and Peter Dayan
0281	A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions Wolfgang Maass and Eduardo D. Sontag
0288	Direct Optimization of Margins Improves Generalization in Combined Classifiers, Llew Mason, Peter L. Bartlett and Jonathan Baxter
0295	On the Optimality of Incremental Neural Network Algorithms, Ron Meir and Vitaly Maiorov
0302	General Bounds on Bayes Errors for Regression with Gaussian Processes, Manfred Upper and Francesco Vivarelli
0309	Mean Field Methods for Classification with Gaussian Processes, Manfred Upper and Ole Winther
0316	On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories, H. C. Rae, Peter Sollich and A. C. C. Coolen
0323	Tight Bounds for the VC-Dimension of Piecewise Polynomial Networks, Akito Sakurai
0330	Shrinking the Tube: A New Support Vector Regression Algorithm, Bernhard Scholkopf, Peter L. Bartlett, Alex J. Smola and Robert Williamson
0337	Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks, N. S. Skantzos, C. F. Beckmann and A. C. C. Coolen
0344	Learning Curves for Gaussian Processes, Peter Sollich

Part IV Algorithms and Architecture

0351	A Theory of Mean Field Approximation, Toshiyuki Tanaka
0361	Learning a Hierarchical Belief Network of Independent Factor Analyzers, Hagai Attias
0368	Semi-Supervised Support Vector Machines, Kristin Bennett and Ayhan Demiriz
0375	Lazy Learning Meets the Recursive Least Squares Algorithm, Mauro Birattari, Gianluca Bontempi and Hugues Bersini
0382	Bayesian PCA, Christopher M. Bishop
0389	Learning Multi-Class Dynamics, Andrew Blake, Ben North and Michael Isard
0396	Approximate Learning of Dynamic Models, Xavier Boyen and Daphne Koller
0403	Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models, Thomas Briegel and Volker Tresp
0410	Global Optimisation of Neural Network Models via Sequential Sampling, Joao F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet and Andrew H. Gee
0417	Efficient Bayesian Parameter Estimation in Large Discrete Domains, Nir Friedman and Yoram Singer
0424	A Randomized Algorithm for Pairwise Clustering, Yoram Gdalyahu, Daphna Weinshall and Michael Werman
0431	Learning Nonlinear Dynamical Systems Using an EM Algorithm, Zoubin Ghahramani and Sam T. Roweis
0438	Classification on Pairwise Proximity Data, Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra and Klaus Obermayer
0445	Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage, Yves Grandvalet and Stephane Canu
0452	Visualizing Group Structure, Marcus Held, Jan Puzicha and Joachim M. Buhmann
0459	Source Separation as a By-Product of Regularization, Sepp Hochreiter and Jurgen Schmidhuber
0466	Learning from Dyadic Data, Thomas Hofmann, Jan Puzicha and Michael I. Jordan
0473	Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation, Aapo Hyvarinen, Patrik Hoyer and Erkki Oja
0480	Restructuring Sparse High Dimensional Data for Effective Retrieval, Charles Lee Isbell, Jr. and Paul Viola
0487	Exploiting Generative Models in Discriminative Classifiers, Tommi S. Jaakkola and David Haussler
0494	Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm, Tony Jebara and Alex Pentland
0501	A Polygonal Line Algorithm for Constructing Principal Curves, Balazs Kegl, Adam Krzyzak, Tamas Linder and Kenneth Zeger
0508	Unsupervised Classification with Non-Gaussian Mixture Models Using ICA, Te-Won Lee, Michael S. Lewicki and Terrence J. Sejnowski
0515	Learning a Continuous Hidden Variable Model for Binary Data, Daniel D. Lee and Haim Sompolinsky
0522	Neural Networks for Density Estimation, Malik Magdon-Ismail and Amir Atiya
0529	Exploratory Data Analysis Using Radial Basis Function Latent Variable Models, Alan D. Marrs and Andrew R. Webb
0536	Kernel PCA and De-Noising in Feature Spaces, Sebastian Mika, Bernhard Scholkopf, Alex J. Smola, Klaus-Robert Muller, Matthias Scholz and Gunnar Ratsch
0543	Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees, Andrew W. Moore
0550	Replicator Equations, Maximal Cliques, and Graph Isomorphism, Marcello Pelillo
0557	Using Analytic QP and Sparseness to Speed Training of Support Vector Machines, John C. Platt
0564	Regularizing AdaBoost, Gunnar Ratsch, Takashi Onoda and Klaus-Robert Muller
0571	Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks, Patrice Y. Simard, Leon Bottou, Patrick Haffner and Yann Le Cun
0578	Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy, Yoram Singer and Manfred K. Warmuth
0585	Semiparametric Support Vector and Linear Programming Machines, Alex J. Smola, Thilo T. Frieß and Bernhard Scholkopf
0592	Probabilistic Visualisation of High-Dimensional Binary Data, Michael E. Tipping
0599	SMEM Algorithm for Mixture Models, Naonori Ueda, Ryohei Nakano, Zoubin Ghabramani and Geoffrey E. Hinton
0606	Learning Mixture Hierarchies, Nuno Vasconcelos and Andrew Lippman
0613	Discovering Hidden Features with Gaussian Processes Regression, Francesco Vivarelli and Christopher K. I. Williams
0620	The Bias-Variance Tradeoff and the Randomized GACV, Grace Wahba, Xiwu Lin, Fangyu Gao, Dong Xiang, Ronald Klein and Barbara Klein
0627	Basis Selection for Wavelet Regression, Kevin R. Wheeler and Atam P. Dhawan
0634	DTs: Dynamic Trees, Christopher K. I. Williams and Nicholas J. Adams
0641	Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours, A. L. Yuille and James M. Coughlan

Part V Implementation

0648	Blind Separation of Filtered Sources Using State-Space Approach, Liqing Zhang and Andrzej Cichocki
0657	Analog VLSI Cellular Implementation of the Boundary Contour System, Gert Cauwenberghs and James Waskiewicz
0664	Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning Capability, Jung-Wook Cho and Soo-Young Lee
0671	A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser, Richard J. Coggins, Raymond J. W. Wang and Marwan A. Jabri
0678	Optimizing Correlation Algorithms for Hardware-Based Transient Classification, R. Timothy Edwards, Gert Cauwenberghs and Fernando J. Pineda
0685	VLSI Implementation of Motion Centroid Localization for Autonomous Navigation, Ralph Etienne-Cummings, Vilctor Gruev and Mohammed Abdel Ghani
0692	A Neuromorphic Monaural Sound Localizer, John G. Harris, Chiang-Jung Pu and Jose C. Principe
0699	An Integrated Vision Sensor for the Computation of Optical Flow Singular Points, Charles M. Higgins and Christof Koch
0706	Computation of Smooth Optical Flow in a Feedback Connected Analog Network, Alan Stocker and Rodney Douglas

Part VI Speech, Handwriting and Signal Processing

0713	A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory, Ping Zhou, Jim Austin and John Kennedy
0723	An Entropic Estimator for Structure Discovery, Matthew Brand
0730	Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations, Michael S. Lewicki and Terrence J. Sejnowski
0737	Controlling the Complexity of HMM Systems by Regularization, Christoph Neukirchen and Gerhard Rigoll
0744	Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs, David A. Nix and John E. Hogden

Part VII Visual Processing

0751	Markov Processes on Curves for Automatic Speech Recognition, Lawrence Saul and Mazin Rahim
0761	A Phase Space Approach to Minimax Entropy Learning and the Minutemax Approximations, James M. Coughlan and A. L. Yuille
0768	Example-Based Image Synthesis of Articulated Figures, Trevor Darrell
0775	Learning to Estimate Scenes from Images, William T. Freeman and Egon C. Pasztor
0782	Learning to Find Pictures of People, Sergey loffe and David Forsyth
0789	Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model, Laurent Itti, Jochen Braun, Dale K. Lee and Christof Koch
0796	A V1 Model of Pop Out and Asymmetty in Visual Search, Zhaoping Li
0803	Support Vector Machines Applied to Face Recognition, P. Jonathon Phillips
0810	Learning Lie Groups for Invariant Visual Perception, Rajesh P. N. Rao and Daniel L. Ruderman
0817	General-Purpose Localization of Textured Image Regions, Ruth Rosenholtz
0824	Probabilistic Image Sensor Fusion, Ravi K. Sharma, Todd K. Leen and Misha Pavel
0831	Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape, Karvel K. Thornber and Lance R. Williams

Part VIII Applications

0838	Classification in Non-Metric Spaces, Daphna Weinshall, David W. Jacobs and Yoram Gdalyahu
0847	Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition, Shumeet Baluja
0854	Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data, Shumeet Baluja
0861	Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields, Dan Cornford, Ian T. Nabney and Christopher K. I. Williams
0868	Vertex Identification in High Energy Physics Experiments, Gideon Dror, Halina Abramowicz and David Horn
0875	Familiarity Discrimination of Radar Pulses, Eric Granger, Stephen Grossberg, Mark A. Rubin and William W. Streilein
0882	Fast Neural Network Emulation of Dynamical Systems for Computer Animation, Radek Grzeszczuk, Demetri Terzopoulos and Geoffrey E. Hinton
0889	Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model, Jaakko Hollmen and Volker Tresp
0896	Graph Matching for Shape Retrieval, Benoit Huet, Andrew D. J. Cross and Edwin R. Hancock
0903	Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts, Amy McGovern and Eliot Moss
0910	Bayesian Modeling of Facial Similarity, Baback Moghaddam, Tony Jebara and Alex Pentland
0917	Reinforcement Learning for Trading, John Moody and Matthew Saffell
0924	Graphical Models for Recognizing Human Interactions, Nuria M. Oliver, Barbara Rosario and Alex Pentland
0931	Independent Component Analysis of Intracellular Calcium Spike Data, Klaus Prank, Julia Borger, Alexander von zur Muhlen, Georg Brabant and Christof Schofl
0938	Applications of Multi-Resolution Neural Networks to Mammography, Clay D. Spence and Paul Sajda
0945	Robot Docking Using Mixtures of Gaussians, Matthew M. Williamson, Roderick Murray-Smith and Volker Hansen

Part IX Control, Navigation and Planning

0952	Using Collective Intelligence to Route Internet Traffic, David H. Wolpert, Kagan Turner and Jeremy Frank
0961	Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm, Mohammad A. Al-Ansari and Ronald J. Williams
0968	Gradient Descent for General Reinforcement Learning, Leemon Baird and Andrew W. Moore
0975	Non-Linear PI Control Inspired by Biological Control Systems, Lyndon J. Brown, Gregory E. Gonye and James S. Schwaber
0982	Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning, Timothy X. Brown, Hui Tong and Satinder Singh
0989	Viewing Classifier Systems as Model Free Learning in POMDPs, Akira Hayashi and Nobuo Suematsu
0996	Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms, Michael Kearns and Satinder Singh
1003	Exploring Unknown Environments with Real-Time Search or Reinforcement Learning, Sven Koenig
1010	The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes, John Loch
1017	Learning Instance-Independent Value Functions to Enhance Local Search, Robert Moll, Andrew G. Barto, Theodore J. Perkins and Richard S. Sutton
1024	Barycentric Interpolators for Continuous Space and Time Reinforcement Learning, Remi Munos and Andrew W. Moore
1031	Risk Sensitive Reinforcement Learning, Ralph Neuneier and Oliver Mihatsch
1038	Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm, Eimei Oyama and Susumu Tachi
1045	Learning Macro-Actions in Reinforcement Learning, Jette Randlov
1052	Reinforcement Learning Based on On-Line EM Algorithm, Masa-aki Sato and Shin Ishii
1059	A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory, Nobuo Suematsu and Akira Hayashi
1066	Improved Switching among Temporally Abstract Actions, Richard S. Sutton, Satinder Singh, Doina Precup and Balaraman Ravindran
1073	Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes, John K. Williams and Satinder Singh
1081	Index of Authors
1085	Keyword Index