Pattern Classification: Neuro-fuzzy Methods and Their Comparison / Edition 1by Shigeo Abe, S. Abe
Pub. Date: 12/11/2000
Publisher: Springer London
This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.
- Springer London
- Publication date:
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.24(d)
Table of Contents
Part I: Introduction. Multilayer Neural Network Classifiers. Support Vector Machines. Membership Functions. Static Fuzzy Rule Generation. Clustering. Tuning of Membership Functions. Robust Pattern Classification. Dynamic Fuzzy Rule Generation. Comparison of Classifier Performance. Optimizing Features. Generation of Training and Test Data Sets.-
Part II: Introduction. Fuzzy Rule Representation and Inference. Fuzzy Rule Generation. Robust Function Approximation.- Appendix A.- Appendix B.
and post it to your social network
Most Helpful Customer Reviews
See all customer reviews >