Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference

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Todd K. Leen, Thomas G. Dietterich, Volker Tresp
MIT Press, 2001 - 1106 pages
The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference.The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference.

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Contents

A Productive Systematic Framework for the Representation of Visual Structure
10
HippocampallyDependent Consolidation in a Hierarchical Model of Neocortex
24
The Use of MDL to Select among Computational Models of Cognition
38
The Early Word Catches the Weights
52
Adaptive Object Representation with HierarchicallyDistributed Memory Sites
66
Dendritic Compartmentalization Could Underlie Competition and Attentional
82
Modelling Spatial Recall Mental Imagery and Neglect
96
Stability and Noise in Biochemical Switches William Bialek
103
NBody Problems in Statistical Learning
521
A SampleBased Criterion
535
Ensemble Learning and Linear Response Theory for ICA
542
Algorithms for Nonnegative Matrix Factorization
556
Constrained Independent Component Analysis Wei Lu and Jagath C Rajapakse
570
The Unscented Particle Filter
584
Automatic Choice of Dimensionality for PCA Thomas P Minka
598
An Information Maximization Approach to Overcomplete and Recurrent
612

A New Model of Spatial Representation in Multimodal Brain Areas
117
Dopamine Bonuses Sham Kakade and Peter Dayan
131
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic
145
Universality and Individuality in a Neural Code Elad Schneidman
159
Interfacing a Silicon Neuron to a Leech Heart
173
Efficient Learning of Linear Perceptrons Shai BenDavid and Hans Ulrich Simon
189
Competition and Arbors in Ocular Dominance Peter Dayan
203
From Margin to Sparsity Thore Graepel Ralf Herbrich and Robert C Williamson
217
On Reversing Jensens Inequality Tony Jebara and Alex Pentland
231
Some New Bounds on the Generalization Error of Combined Classifiers
245
Foundations for a Circuit Complexity Theory of Sensory Processing
259
A Framework for Good
273
Simulations With Field Theoretic Priors
287
The Kernel Trick for Distances Bernhard Schölkopf
301
Analysis of Bit Error Probability of DirectSequence CDMA Multiuser
315
Algebraic Information Geometry for Learning Machines with Singularities
329
Stagewise Processing in Errorcorrecting Codes and Image Restoration
343
Convergence of Large Margin Separable Linear Classification Tong Zhang
357
A Variational MeanField Theory for Sigmoidal Belief Networks
374
Model Complexity Goodness of Fit and Diminishing Returns
388
A Linear Programming Approach to Novelty Detection
395
Backpropagation Conjugate Gradient and Early
402
Incremental and Decremental Support Vector Machine Learning
409
Vicinal Risk Minimization
416
The Missing Link A Probabilistic Model of Document Content and Hypertext
430
Improved Output Coding for Classification Using Continuous Relaxation
437
Lehel Csató and Manfred Opper
444
An Adaptive Metric Machine for Pattern Classification
458
Hightemperature Expansions for Learning Models of Nonnegative Data
465
Incorporating SecondOrder Functional Knowledge for Better Option Pricing
472
A StructureBased Approach
479
Suitors of Local Probability Propagation
486
Sequentially Fitting Inclusive Trees for Inference in NoisyOR Networks
493
A New Approximate Maximal Margin Classification Algorithm Claudio Gentile
500
Propagation Algorithms for Variational Bayesian Learning
507
Kernel Expansions with Unlabeled Examples
626
Data Clustering by Markovian Relaxation and the Information Bottleneck
640
Mixtures of Gaussian Processes Volker Tresp
654
Feature Selection for SVMs Jason Weston Sayan Mukherjee Olivier Chapelle
668
Using the Nyström Method to Speed Up Kernel Machines
682
A GradientBased Boosting Algorithm for Regression Problems
696
A Silicon Primitive for Competitive Learning
713
Homeostasis in a Silicon Integrate and Fire Neuron
727
Fourlegged Walking Gait Control Using a Neuromorphic Chip Interfaced to
741
Speech Denoising and Dereverberation Using Probabilistic Models
758
Learning Joint Statistical Models for AudioVisual Fusion and Segregation
772
HigherOrder Statistical Properties Arising from the NonStationarity of Natural
786
Minimum Bayes Error Feature Selection for Continuous Speech Recognition
800
A Linear Operator for Measuring Synchronization of Video Facial
814
Noise Suppression Based on Neurophysiologicallymotivated SNR Estimation
821
Emergence of Movement Sensitive Neurons Properties by Learning a Sparse
838
A Markov Chain Monte Carlo Approach
852
Color Opponency Constitutes a Sparse Representation for the Chromatic
866
Partially Observable SDE Models for Image Sequence Recognition Tasks
880
Learning and Tracking Cyclic Human Motion
894
Ratecoded Restricted Boltzmann Machines for Face Recognition
908
From Mixtures of Mixtures to Adaptive Transform Coding
925
A Comparison of Image Processing Techniques for Visual Speech Recognition
939
Recognizing Handwritten Digits Using Hierarchical Products of Experts
953
Probabilistic Semantic Video Indexing
967
Learning Switching Linear Models of Human Motion
981
The Use of Classifiers in Sequential Inference Vasin Punyakanok and Dan Roth
1002
Programmable Reinforcement Learning Agents David Andre and Stuart J Russell
1019
Decomposition of Reinforcement Learning for Admission Control of SelfSimilar
1033
Hierarchical MemoryBased Reinforcement Learning
1047
Robust Reinforcement Learning Jun Morimoto and Kenji Doya
1061
Using Free Energies to Represent Qvalues in a Multiagent Reinforcement
1075
Approximate Policy Construction Using Decision Diagrams
1089
Index of Authors
1097
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About the author (2001)

Todd K. Leen is Professor of Computer Science and Engineering, and of Electrical and Computer Engineering, at Oregon Graduate Institute of Science and Technology. Thomas G. Dietterich is Professor of Computer Science at Oregon State University. Volker Tresp heads a research group at Siemens Corporate Technology in Munich.

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