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Other titles in the Computational Neuroscience series:

Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)

by

Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

Since its founding in 1989 by Terrence Sejnowski,

Synopsis:

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.

About 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.

Product Details

ISBN:
9780262581684
Editor:
Hinton, Geoffrey
Editor:
Sejnowski, Terrence J.
Editor:
Hinton, Geoffrey
Editor:
Sejnowski, Terrence J.
Author:
Sejnowski, Terrence J.
Author:
ski, Terrence J.
Author:
Sejnow
Author:
Hinton, Geoffrey
Publisher:
MIT Press (MA)
Location:
Cambridge, Mass. :
Subject:
Computer Science
Subject:
Neuropsychology
Subject:
Neuroscience
Subject:
Learning
Subject:
Neural networks (computer science)
Subject:
Neural computers
Subject:
Health and Medicine-Medical Specialties
Edition Description:
Trade paper
Series:
Computational Neuroscience Unsupervised Learning
Publication Date:
19990531
Binding:
TRADE PAPER
Grade Level:
from 17
Language:
English
Illustrations:
Yes
Pages:
414
Dimensions:
8.9 x 5.9 x 0.9 in

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Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience) New Trade Paper
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Product details 414 pages Bradford Book - English 9780262581684 Reviews:
"Synopsis" by , Since its founding in 1989 by Terrence Sejnowski,
"Synopsis" by , 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.
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