This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area. Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. It also contains several interesting IT, engineering and bioinformatics applications.
Sample Chapter(s)
Chapter 1: Introduction (1,046 KB)
Contents:
- Learning Performance and Enhancement
- Generalization and Performance Enhancement
- Basis Function Networks for Classification
- Self-Organizing Maps
- Classification and Feature Selection
- Engineering Applications
Readership: Postgraduates and researchers in neural networks, pattern recognition and electrical and electronic engineering.
