Neural Networks and Statistical Learning

by M N S Swamy, Ke-Lin Du, M. N. S. Swamy

Book Overview

Introduction.- Fundamentals of Machine Learning.- Perceptrons.- Multilayer perceptrons: architecture and error backpropagation.- Multilayer perceptrons: other learing techniques.- Hopfield networks, simulated annealing and chaotic neural networks.- Associative memory networks.- Clustering I: Basic clustering models and algorithms.- Clustering II: topics in clustering.- Radial basis function networks.- Recurrent neural networks.- Principal component analysis.- Nonnegative matrix factorization and compressed sensing.- Independent component analysis.- Discriminant analysis.- Support vector machines.- Other kernel methods.- Reinforcement learning.- Probabilistic and Bayesian networks.- Combining multiple learners: data fusion and emsemble learning.- Introduction of fuzzy sets and logic.- Neurofuzzy systems.- Neural circuits.- Pattern recognition for biometrics and bioinformatics.- Data mining.- Appenidx A. Mathematical Preliminaries.- Appendix B. Benchmarks and resources.

• Format: Paperback
• Language: English
• ISBN: 1447174542
• ISBN13: 9781447174547
• Release Date: September 2020
• Publisher: Springer
• Length: 988 Pages
• Weight: 3.08 lbs.
• Dimensions: 2.0" x 6.1" x 9.2"