Neural Networks and Learning Machines / Simon Haykin.

By:

Contributor(s):

Haykin, Simon S, 1931-. Neural networks

Material type:

TextPublication details: Noida : Pearson India Education Services Pvt. Ltd., 2026.Edition: 3rd edDescription: xxx, 906 p; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780131471399
0131471392

Subject(s):

DDC classification:

006.32 HAY-N3

Online resources:

Table of contents only

Contents:

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

Summary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Books	ARCHBISHOP KAVUKATTU CENTRAL LIBRARY				006.32 SIN-N3 (Browse shelf(Opens below))					Available			69317

Total holds: 0

Browsing ARCHBISHOP KAVUKATTU CENTRAL LIBRARY shelves Close shelf browser (Hides shelf browser)

Previous							No cover image available	Next
Previous	006.32 RAJ-N Neural Networks, Fuzzy Systems, And Evolutionary Algorithms Synthesis and Applications	006.32 RAJ-N2 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms Synthesis and Applications	006.32 RAJ-N2 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms Synthesis and Applications /	006.32 SIN-N3 Neural Networks and Learning Machines /	006.32 YEN-A Artifical Neural Networks	006.32 YEN-A Artifical Neural Networks /	006.35 PAT-M Mastering LLM Applications with LangChain and Hugging Face: Practical Insights into LLM Deployment and Use Cases /	Next

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.

There are no comments on this title.

to post a comment.