Neural Networks and Learning Machines /
Haykin, Simon S., 1931-
Neural Networks and Learning Machines / Simon Haykin. - 3rd ed. - Noida : Pearson India Education Services Pvt. Ltd., 2026. - xxx, 906 p. : 24 cm.
Rev. ed of: Neural networks. 2nd ed., c1999.
Includes bibliographical references (p. 847-887) and index.
Chapter 1 Rosenblatt's Perceptron
Chapter 2 Model Building through Regression
Chapter 3 The Least-Mean-Square Algorithm
Chapter 4 Multilayer Perceptrons
Chapter 5 Kernel Methods and Radial-Basis Function Networks
Chapter 6 Support Vector Machines
Chapter 7 Regularization Theory
Chapter 8 Principal-Components Analysis
Chapter 9 Self-Organizing Maps
Chapter 10 Information-Theoretic Learning Models
Chapter 11 Stochastic Methods Rooted in Statistical Mechanics
Chapter 12 Dynamic Programming
Chapter 13 Neurodynamics
Chapter 14 Bayseian Filtering for State Estimation of Dynamic Systems
Chapter 15 Dynamically Driven Recurrent Networks
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.
9780131471399 0131471392
Computer Science
AI
Artificial Intelligence
Neural networks (Computer science)
Adaptive filters.
006.32 / HAY-N3
Neural Networks and Learning Machines / Simon Haykin. - 3rd ed. - Noida : Pearson India Education Services Pvt. Ltd., 2026. - xxx, 906 p. : 24 cm.
Rev. ed of: Neural networks. 2nd ed., c1999.
Includes bibliographical references (p. 847-887) and index.
Chapter 1 Rosenblatt's Perceptron
Chapter 2 Model Building through Regression
Chapter 3 The Least-Mean-Square Algorithm
Chapter 4 Multilayer Perceptrons
Chapter 5 Kernel Methods and Radial-Basis Function Networks
Chapter 6 Support Vector Machines
Chapter 7 Regularization Theory
Chapter 8 Principal-Components Analysis
Chapter 9 Self-Organizing Maps
Chapter 10 Information-Theoretic Learning Models
Chapter 11 Stochastic Methods Rooted in Statistical Mechanics
Chapter 12 Dynamic Programming
Chapter 13 Neurodynamics
Chapter 14 Bayseian Filtering for State Estimation of Dynamic Systems
Chapter 15 Dynamically Driven Recurrent Networks
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.
9780131471399 0131471392
Computer Science
AI
Artificial Intelligence
Neural networks (Computer science)
Adaptive filters.
006.32 / HAY-N3