Unsupervised Learning

Foundations of Neural Computation

By: Geoffrey Hinton

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Paperback | 24 May 1999

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416 Pages

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18+

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23.0 x 15.2 x 2.4

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Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. "Foundations of Neural Computation"collects, 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.

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You Can Find This Book In

Non-Fiction Medicine Clinical & Internal Medicine Neurology & Clinical Neurophysiology Psychology Physiological & Neuro-Psychology, Biopsychology Science Biology, Life Sciences Life Sciences in General Neurosciences Computing & I.T.Graphical & Digital Media Applications

3D Graphics & Modelling Computer Science Mathematical Theory of Computation Artificial Intelligence Neural Networks & Fuzzy Systems

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

Unsupervised Learning

Foundations of Neural Computation

At a Glance

Paperback

Shipping

How to return your order

Defective items

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