An Information Theoretic Approach To Neural Computing

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An Information-Theoretic Approach to Neural Computing

Gustavo Deco,Dragan Obradovic
An Information Theoretic Approach to Neural Computing Book PDF

Author : Gustavo Deco,Dragan Obradovic
Publisher : Springer Science & Business Media
Page : 265 pages
File Size : 41,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461240167

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An Information-Theoretic Approach to Neural Computing by Gustavo Deco,Dragan Obradovic Pdf

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Information-Theoretic Aspects of Neural Networks

P. S. Neelakanta
Information Theoretic Aspects of Neural Networks Book PDF

Author : P. S. Neelakanta
Publisher : CRC Press
Page : 417 pages
File Size : 53,6 Mb
Release : 2020-09-23
Category : History
ISBN : 9781000102758

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Information-Theoretic Aspects of Neural Networks by P. S. Neelakanta Pdf

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Information Theoretic Neural Computation

Ryotaro Kamimura
Information Theoretic Neural Computation Book PDF

Author : Ryotaro Kamimura
Publisher : World Scientific
Page : 220 pages
File Size : 55,9 Mb
Release : 2002-12-19
Category : Computers
ISBN : 9789814494274

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Information Theoretic Neural Computation by Ryotaro Kamimura Pdf

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. α-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. Contents: Information in Neural NetworksInformation MinimizationInformation MaximizationConstrained Information MaximizationNeural Feature DetectorsInformation Maximization and MinimizationInformation ControllerInformation Control by α-EntropyIntegrated Information Processing Systems Readership: Students and researchers in artificial intelligence and neural networks. Keywords:

Information Theoretic Learning

Jose C. Principe
Information Theoretic Learning Book PDF

Author : Jose C. Principe
Publisher : Springer Science & Business Media
Page : 538 pages
File Size : 42,7 Mb
Release : 2010-04-06
Category : Computers
ISBN : 9781441915702

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Information Theoretic Learning by Jose C. Principe Pdf

This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Computer Vision - ECCV 2002

Anders Heyden,Gunnar Sparr,Mads Nielsen,Peters Johansen
Computer Vision ECCV 2002 Book PDF

Author : Anders Heyden,Gunnar Sparr,Mads Nielsen,Peters Johansen
Publisher : Springer
Page : 919 pages
File Size : 53,7 Mb
Release : 2003-08-02
Category : Computers
ISBN : 9783540479772

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Computer Vision - ECCV 2002 by Anders Heyden,Gunnar Sparr,Mads Nielsen,Peters Johansen Pdf

Premiering in 1990 in Antibes, France, the European Conference on Computer Vision, ECCV, has been held biennially at venues all around Europe. These conferences have been very successful, making ECCV a major event to the computer vision community. ECCV 2002 was the seventh in the series. The privilege of organizing it was shared by three universities: The IT University of Copenhagen, the University of Copenhagen, and Lund University, with the conference venue in Copenhagen. These universities lie ̈ geographically close in the vivid Oresund region, which lies partly in Denmark and partly in Sweden, with the newly built bridge (opened summer 2000) crossing the sound that formerly divided the countries. We are very happy to report that this year’s conference attracted more papers than ever before, with around 600 submissions. Still, together with the conference board, we decided to keep the tradition of holding ECCV as a single track conference. Each paper was anonymously refereed by three different reviewers. For the nal selection, for the rst time for ECCV, a system with area chairs was used. These met with the program chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpresentations and 181 posters.Also at this meeting the selection was made without knowledge of the authors’identity.

System Parameter Identification

Badong Chen,Yu Zhu,Jinchun Hu,Jose C. Principe
System Parameter Identification Book PDF

Author : Badong Chen,Yu Zhu,Jinchun Hu,Jose C. Principe
Publisher : Newnes
Page : 266 pages
File Size : 40,9 Mb
Release : 2013-07-17
Category : Computers
ISBN : 9780124045958

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System Parameter Identification by Badong Chen,Yu Zhu,Jinchun Hu,Jose C. Principe Pdf

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors’ research provides a base for the book, but it incorporates the results from the latest international research publications. Named a 2013 Notable Computer Book for Information Systems by Computing Reviews One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments Contains numerous illustrative examples to help the reader grasp basic methods

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

Okyay Kaynak,Ethem Alpaydin,Erkki Oja,Lei Xu
Artificial Neural Networks and Neural Information Processing ICANN ICONIP 2003 Book PDF

Author : Okyay Kaynak,Ethem Alpaydin,Erkki Oja,Lei Xu
Publisher : Springer
Page : 1194 pages
File Size : 50,8 Mb
Release : 2003-08-03
Category : Computers
ISBN : 9783540449898

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Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 by Okyay Kaynak,Ethem Alpaydin,Erkki Oja,Lei Xu Pdf

The refereed proceedings of the Joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003. The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.

Information Theory, Inference and Learning Algorithms

David J. C. MacKay
Information Theory Inference and Learning Algorithms Book PDF

Author : David J. C. MacKay
Publisher : Cambridge University Press
Page : 694 pages
File Size : 43,8 Mb
Release : 2003-09-25
Category : Computers
ISBN : 0521642981

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Information Theory, Inference and Learning Algorithms by David J. C. MacKay Pdf

Table of contents

Information-Theoretic Aspects of Neural Networks

P. S. Neelakanta
Information Theoretic Aspects of Neural Networks Book PDF

Author : P. S. Neelakanta
Publisher : CRC Press
Page : 416 pages
File Size : 40,7 Mb
Release : 1999-03-30
Category : Computers
ISBN : 0849331986

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Information-Theoretic Aspects of Neural Networks by P. S. Neelakanta Pdf

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Minimum Error Entropy Classification

Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre
Minimum Error Entropy Classification Book PDF

Author : Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre
Publisher : Springer
Page : 262 pages
File Size : 46,8 Mb
Release : 2012-07-25
Category : Technology & Engineering
ISBN : 9783642290299

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Minimum Error Entropy Classification by Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre Pdf

This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

Mathematical Approaches to Neural Networks

J.G. Taylor
Mathematical Approaches to Neural Networks Book PDF

Author : J.G. Taylor
Publisher : Elsevier
Page : 381 pages
File Size : 42,7 Mb
Release : 1993-10-27
Category : Computers
ISBN : 0080887392

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Mathematical Approaches to Neural Networks by J.G. Taylor Pdf

The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.

Introduction To The Theory Of Neural Computation

John A. Hertz
Introduction To The Theory Of Neural Computation Book PDF

Author : John A. Hertz
Publisher : CRC Press
Page : 352 pages
File Size : 46,7 Mb
Release : 2018-03-08
Category : Science
ISBN : 9780429968211

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Introduction To The Theory Of Neural Computation by John A. Hertz Pdf

Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.

Engineering Applications of Bio-Inspired Artificial Neural Networks

Jose Mira,Juan V. Sanchez-Andres
Engineering Applications of Bio Inspired Artificial Neural Networks Book PDF

Author : Jose Mira,Juan V. Sanchez-Andres
Publisher : Springer Science & Business Media
Page : 942 pages
File Size : 49,7 Mb
Release : 1999-05-19
Category : Computers
ISBN : 3540660682

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Engineering Applications of Bio-Inspired Artificial Neural Networks by Jose Mira,Juan V. Sanchez-Andres Pdf

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial and Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation and implementation, image processing, and engineering applications.

The Principles of Deep Learning Theory

Daniel A. Roberts,Sho Yaida,Boris Hanin
The Principles of Deep Learning Theory Book PDF

Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 43,7 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781316519332

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The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin Pdf

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Engineering Applications of Neural Networks

Chrisina Jayne,Shigang Yue,Lazaros S. Iliadis
Engineering Applications of Neural Networks Book PDF

Author : Chrisina Jayne,Shigang Yue,Lazaros S. Iliadis
Publisher : Springer
Page : 512 pages
File Size : 55,9 Mb
Release : 2013-04-19
Category : Computers
ISBN : 9783642329098

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Engineering Applications of Neural Networks by Chrisina Jayne,Shigang Yue,Lazaros S. Iliadis Pdf

This book constitutes the refereed proceedings of the 13th International Conference on Engineering Applications of Neural Networks, EANN 2012, held in London, UK, in September 2012. The 49 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers describe the applications of neural networks and other computational intelligence approaches to intelligent transport, environmental engineering, computer security, civil engineering, financial forecasting, virtual learning environments, language interpretation, bioinformatics and general engineering.