Unsupervised Learning: Foundations of Neural Computation / Edition 1

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Overview

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computationcollects,by topic, the most significant papers that have appeared in the journal over the past nine years.This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

Disc. specific research approaches; incl. algorithmic repre- sentations, linear diagrams & 3-D conceptualizations.

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Product Details

  • ISBN-13: 9780262581684
  • Publisher: MIT Press
  • Publication date: 6/11/1999
  • Series: Computational Neuroscience
  • Edition description: First Edition
  • Edition number: 1
  • Pages: 414
  • Product dimensions: 6.00 (w) x 9.00 (h) x 0.90 (d)

Meet the Author

Geoffrey Hinton is Professor of Computer Science at the University of Toronto.

Terrence J. Sejnowski is Francis Crick Professor, Director of the Computational Neurobiology Laboratory, and a Howard Hughes Medical Institute Investigator at the Salk Institute for Biological Studies and Professor of Biology at the University of California, San Diego.

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Table of Contents

Introduction
1 Unsupervised Learning 1
2 Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network 19
3 Convergent Algorithm for Sensory Receptive Field Development 31
4 Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis 47
5 Learning Invariance from Transformation Sequences 63
6 Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints 71
7 What Is the Goal of Sensory Coding? 101
8 An Information-Maximization Approach to Blind Separation and Blind Deconvolution 145
9 Natural Gradient Works Efficiently in Learning 177
10 A Fast Fixed-Point Algorithm for Independent Component Analysis 203
11 Feature Extraction Using an Unsupervised Neural Network 213
12 Learning Mixture Models of Spatial Coherence 223
13 Bayesian Self-Organization Driven by Prior Probability Distributions 235
14 Finding Minimum Entropy Codes 249
15 Learning Population Codes by Minimizing Description Length 261
16 The Helmholtz Machine 277
17 Factor Analysis Using Delta-Rule Wake-Sleep Learning 293
18 Dimension Reduction by Local Principal Component Analysis 317
19 A Resource-Allocating Network for Function Interpolation 341
20 Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures 355
21 Learning to Generalize from Single Examples in the Dynamic Link Architecture 373
Index 391
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