Neural Networks and Learning Machines; Simon Haykin; 2009
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Neural Networks and Learning Machines Upplaga 3

av Simon Haykin
Neural Networks and Learning Machines

Third Edition

Simon Haykin

McMaster University, Canada

 

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.

Kernel methods, including support vector machines, and the representer theorem.

Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.

Stochastic dynamic programming, including approximate and neurodynamic procedures.

Sequential state-estimation algorithms, including Kalman and particle filters.

Recurrent neural networks trained using sequential-state estimation algorithms.

Insightful computer-oriented experiments.

 

Just as importantly, the book is written in a readable style that is Simon Haykins hallmark.
Neural Networks and Learning Machines

Third Edition

Simon Haykin

McMaster University, Canada

 

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.

Kernel methods, including support vector machines, and the representer theorem.

Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.

Stochastic dynamic programming, including approximate and neurodynamic procedures.

Sequential state-estimation algorithms, including Kalman and particle filters.

Recurrent neural networks trained using sequential-state estimation algorithms.

Insightful computer-oriented experiments.

 

Just as importantly, the book is written in a readable style that is Simon Haykins hallmark.
Upplaga: 3e upplagan
Utgiven: 2009
ISBN: 9780131471399
Förlag: Pearson
Format: Inbunden
Språk: Engelska
Sidor: 936 st
Neural Networks and Learning Machines

Third Edition

Simon Haykin

McMaster University, Canada

 

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.

Kernel methods, including support vector machines, and the representer theorem.

Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.

Stochastic dynamic programming, including approximate and neurodynamic procedures.

Sequential state-estimation algorithms, including Kalman and particle filters.

Recurrent neural networks trained using sequential-state estimation algorithms.

Insightful computer-oriented experiments.

 

Just as importantly, the book is written in a readable style that is Simon Haykins hallmark.
Neural Networks and Learning Machines

Third Edition

Simon Haykin

McMaster University, Canada

 

This third edition of a classic book presents a comprehensive treatment of neural networks and learning machines. These two pillars that are closely related. The book has been revised extensively to provide an up-to-date treatment of a subject that is continually growing in importance. Distinctive features of the book include:

On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scale learning problems.

Kernel methods, including support vector machines, and the representer theorem.

Information-theoretic learning models, including copulas, independent components analysis (ICA), coherent ICA, and information bottleneck.

Stochastic dynamic programming, including approximate and neurodynamic procedures.

Sequential state-estimation algorithms, including Kalman and particle filters.

Recurrent neural networks trained using sequential-state estimation algorithms.

Insightful computer-oriented experiments.

 

Just as importantly, the book is written in a readable style that is Simon Haykins hallmark.
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Om Neural Networks and Learning Machines (2009)

I mars 2009 släpptes boken Neural Networks and Learning Machines skriven av Simon Haykin. Det är den 3e upplagan av kursboken. Den är skriven på engelska och består av 936 sidor djupgående information. Förlaget bakom boken är Pearson som har sitt säte i London.

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