Название: Neural Networks and Statistical Learning
Автор: Du K.L., Swamy M.N.S.
Издательство: 2nd. ed. — Springer-Verlag London
Год: 2019
Формат: pdf
Страниц: 955
Для сайта: LitMy.ru
Размер: 12 mb
Язык: английский
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. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:
Multilayer perceptron.
Associative memory.
Clustering.
Reinforcement learning.
Probabilistic and Bayesian networks.
Fuzzy sets and logic.
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.
Preface to the Second Edition.
Preface to the First Edition.
Introduction.
Fundamentals of Machine Learning.
Elements of Computational Learning Theory.
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.
Independent Component Analysis.
Discriminant Analysis.
Reinforcement Learning.
Compressed Sensing and Dictionary Learning.
Matrix Completion.
Kernel Methods.
Support Vector Machines.
Probabilistic and Bayesian Networks.
Boltzmann Machines.
Deep Learning.
Combining Multiple Learners: Data Fusion and Ensemble Learning.
Introduction to Fuzzy Sets and Logic.
Neurofuzzy Systems.
Neural Network Circuits and Parallel Implementations.
Pattern Recognition for Biometrics and Bioinformatics.
Data Mining.
Big Data, Cloud Computing, and Internet of Things.
Appendix A: Mathematical Preliminaries.
Appendix B: Benchmarks and Resources.
Index.