Description
Springer Neural Networks And Statistical Learning 2nd Edition 2019 by Du K.L.
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 exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing._x000D__x000D_Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:_x000D__x000D__x000D__x000D_* multilayer perceptron;_x000D_* the Hopfield network;_x000D_* associative memory models;* clustering models and algorithms;_x000D_* t he radial basis function network;_x000D_* recurrent neural networks;_x000D_* nonnegative matrix factorization;_x000D_* independent component analysis;_x000D_*probabilistic and Bayesian networks; and_x000D_* fuzzy sets and logic._x000D__x000D__x000D__x000D__x000D_Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning._x000D_ 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.- Appenidx A. Mathematical Preliminaries.- Appendix B. Benchmarks and resources.