Neural Networks and Statistical Learning

• Main Authors: Du, Ke-Lin (Author, http://id.loc.gov/vocabulary/relators/aut), Swamy, M. N. S. (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: London : Springer London : Imprint: Springer, 2019.
• Edition: 2nd ed. 2019.
• Subjects: Neural networks (Computer science) .; Computational intelligence.; Artificial intelligence.; Pattern recognition.; Signal processing.; Image processing.; Speech processing systems.; Mathematical Models of Cognitive Processes and Neural Networks.; Computational Intelligence.; Artificial Intelligence.; Pattern Recognition.; Signal, Image and Speech Processing.

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercise...

Table of Contents:

• 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.