Advances in Neural Information Processing Systems 13: Proceedings of the 2000 ConferenceTodd 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. |
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 |
1097 | |
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Advances in Neural Information Processing Systems 13 Todd K. Leen,Thomas G. Dietterich,Volker Tresp No preview available - 2001 |
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Common terms and phrases
active Advances in Neural analysis applied approach approximation basis functions Bayesian Boltzmann machines bound cells classifier clustering complexity components convergence correlation corresponding covariance covariance matrix data set defined denotes density dimensional distribution dynamics eigenvalues equation error estimate example feature space Figure filter frequency Gaussian processes given hippocampus IEEE independent component analysis Information Processing Systems input iterative kernel kernel PCA layer learning algorithm linear Machine Learning margin matrix maximal method minimize mutual information Neural Information Processing neural networks neurons nodes noise nonlinear obtained optimal output overfitting parameters patterns perceptron performance pixels points posterior prediction prior probabilistic probability problem random recognition representation samples sequence shown signal solution speech spike statistical stimulus structure support vector machines synapses Theorem theory training data training set transform unsupervised learning update values variance visual weights