Neural networks and learning machines /
Haykin, Simon S., 1931-
Neural networks and learning machines / Simon Haykin. - 3rd ed. / International ed. - Harlow ; London : Pearson Education, 2009. - 934 p. : ill. ; 23 cm.
Previous ed.: published as Neural networks. Upper Saddle River, N.J.: Prentice Hall, 1999.
Includes bibliographical references and index.
Preface x -- Introduction 1 -- Chapter 1 Rosenblatt’s Perceptron 47 -- Chapter 2 Model Building through Regression 68 -- Chapter 3 The Least-Mean-Square Algorithm 91 -- Chapter 4 Multilayer Perceptrons 122 -- Chapter 5 Kernel Methods and Radial-Basis Function Networks 230 -- Chapter 6 Support Vector Machines 268 -- Chapter 7 Regularization Theory 313 -- Chapter 8 Principal-Components Analysis 367 -- Chapter 9 Self-Organizing Maps 425 -- Chapter 10 Information-Theoretic Learning Models 475 -- Chapter 11 Stochastic Methods Rooted in Statistical Mechanics 579 -- Chapter 12 Dynamic Programming 627 -- Chapter 13 Neurodynamics 672 -- Chapter 14 Bayseian Filtering for State Estimation of Dynamic Systems 731 -- Chapter 15 Dynamically Driven Recurrent Networks 790
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. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ 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.
9780131293762 0131293761
Neural networks (Computer science)
Neural networks (Computer science) -- Problems, exercises, etc.
006.32
Neural networks and learning machines / Simon Haykin. - 3rd ed. / International ed. - Harlow ; London : Pearson Education, 2009. - 934 p. : ill. ; 23 cm.
Previous ed.: published as Neural networks. Upper Saddle River, N.J.: Prentice Hall, 1999.
Includes bibliographical references and index.
Preface x -- Introduction 1 -- Chapter 1 Rosenblatt’s Perceptron 47 -- Chapter 2 Model Building through Regression 68 -- Chapter 3 The Least-Mean-Square Algorithm 91 -- Chapter 4 Multilayer Perceptrons 122 -- Chapter 5 Kernel Methods and Radial-Basis Function Networks 230 -- Chapter 6 Support Vector Machines 268 -- Chapter 7 Regularization Theory 313 -- Chapter 8 Principal-Components Analysis 367 -- Chapter 9 Self-Organizing Maps 425 -- Chapter 10 Information-Theoretic Learning Models 475 -- Chapter 11 Stochastic Methods Rooted in Statistical Mechanics 579 -- Chapter 12 Dynamic Programming 627 -- Chapter 13 Neurodynamics 672 -- Chapter 14 Bayseian Filtering for State Estimation of Dynamic Systems 731 -- Chapter 15 Dynamically Driven Recurrent Networks 790
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. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ 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.
9780131293762 0131293761
Neural networks (Computer science)
Neural networks (Computer science) -- Problems, exercises, etc.
006.32