Information theoretic neural computation:
Gespeichert in:
1. Verfasser: | |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Singapore
World Scientific
©2002
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Schlagworte: | |
Online-Zugang: | FAW01 FAW02 Volltext |
Beschreibung: | Includes bibliographical references (pages 197-203) 1. Introduction -- 2. Information in Neural Networks -- 3. Information Minimization -- 4. Information Maximization -- 5. Constrained Information Maximization -- 6. Neural Feature Detectors -- 7. Information Maximization and Minimization -- 8. Information Controller -- 9. Information Control by [alpha]-Entropy -- 10. Integrated Information Processing Systems -- App. A. Data Description -- App. B. Sonority In order to develop new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. a-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind |
Beschreibung: | 1 Online-Ressource |
ISBN: | 9789810240752 9789812778598 9810240759 9812778594 |
Internformat
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500 | |a Includes bibliographical references (pages 197-203) | ||
500 | |a 1. Introduction -- 2. Information in Neural Networks -- 3. Information Minimization -- 4. Information Maximization -- 5. Constrained Information Maximization -- 6. Neural Feature Detectors -- 7. Information Maximization and Minimization -- 8. Information Controller -- 9. Information Control by [alpha]-Entropy -- 10. Integrated Information Processing Systems -- App. A. Data Description -- App. B. Sonority | ||
500 | |a In order to develop new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. a-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind | ||
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650 | 4 | |a Entropy (Information theory) | |
650 | 4 | |a Neural computers | |
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Datensatz im Suchindex
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any_adam_object | |
author | Kamimura, Ryotaro |
author_facet | Kamimura, Ryotaro |
author_role | aut |
author_sort | Kamimura, Ryotaro |
author_variant | r k rk |
building | Verbundindex |
bvnumber | BV043145311 |
collection | ZDB-4-EBA |
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dewey-full | 006.32 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 006 - Special computer methods |
dewey-raw | 006.32 |
dewey-search | 006.32 |
dewey-sort | 16.32 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik |
format | Electronic eBook |
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indexdate | 2024-07-10T07:18:50Z |
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isbn | 9789810240752 9789812778598 9810240759 9812778594 |
language | English |
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spelling | Kamimura, Ryotaro Verfasser aut Information theoretic neural computation Ryotaro Kamimura Singapore World Scientific ©2002 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (pages 197-203) 1. Introduction -- 2. Information in Neural Networks -- 3. Information Minimization -- 4. Information Maximization -- 5. Constrained Information Maximization -- 6. Neural Feature Detectors -- 7. Information Maximization and Minimization -- 8. Information Controller -- 9. Information Control by [alpha]-Entropy -- 10. Integrated Information Processing Systems -- App. A. Data Description -- App. B. Sonority In order to develop new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. a-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind COMPUTERS / Neural Networks bisacsh Automatic speech recognition fast Entropy (Information theory) fast Neural computers fast Neural networks (Computer science) fast Automatic speech recognition Entropy (Information theory) Neural computers Neural networks (Computer science) http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=514089 Aggregator Volltext |
spellingShingle | Kamimura, Ryotaro Information theoretic neural computation COMPUTERS / Neural Networks bisacsh Automatic speech recognition fast Entropy (Information theory) fast Neural computers fast Neural networks (Computer science) fast Automatic speech recognition Entropy (Information theory) Neural computers Neural networks (Computer science) |
title | Information theoretic neural computation |
title_auth | Information theoretic neural computation |
title_exact_search | Information theoretic neural computation |
title_full | Information theoretic neural computation Ryotaro Kamimura |
title_fullStr | Information theoretic neural computation Ryotaro Kamimura |
title_full_unstemmed | Information theoretic neural computation Ryotaro Kamimura |
title_short | Information theoretic neural computation |
title_sort | information theoretic neural computation |
topic | COMPUTERS / Neural Networks bisacsh Automatic speech recognition fast Entropy (Information theory) fast Neural computers fast Neural networks (Computer science) fast Automatic speech recognition Entropy (Information theory) Neural computers Neural networks (Computer science) |
topic_facet | COMPUTERS / Neural Networks Automatic speech recognition Entropy (Information theory) Neural computers Neural networks (Computer science) |
url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=514089 |
work_keys_str_mv | AT kamimuraryotaro informationtheoreticneuralcomputation |