Neural Networks And Learning Machines

by Simon 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 hyb

Genres: Artificial Intelligence, Computer Science, Programming

146 pages, Paperback

First published September 13, 1993

About the author

Simon Haykin was a Canadian electrical engineer noted for his pioneering work in Adaptive Signal Processing with emphasis on applications to Radar Engineering and Telecom Technology. He was a Distinguished University Professor at McMaster University in Hamilton, Ontario, Canada.

Reviews

[Shyam Poovaiah]

A good ML book for those interested in Mathematical Proofs and completeness.

[zedoul]

Recommendable. It concentrates on Neural network, including various approaches that I did not considered as Neural network - reinforcement learning, Kalman filter for instance. Explanations are relatively good.