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borderland which separates the known from the unknown”

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Neural networks and learning machines/ Simon Haykin

By: Material type: TextTextPublication details: New York: Prentice Hall, 2009Edition: 3rdDescription: xxx, 366 pages; 18 cmISBN:
  • 9780131471399
  • 0131471392
Subject(s): DDC classification:
  • 23 006.32019 H419
Contents:
Introduction -- Rosenblatt's perceptron -- Model building through regression -- The Least-Mean-Square algorithm -- Multilayer perceptrons -- Kernel methods and radial-basis function networks -- Support vector machines -- Regularization theory
Summary: For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
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This book is completed in two parts. The 2nd part starts from chapter 8.

Introduction -- Rosenblatt's perceptron -- Model building through regression -- The Least-Mean-Square algorithm -- Multilayer perceptrons -- Kernel methods and radial-basis function networks -- Support vector machines -- Regularization theory

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

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