Neural Networks and Statistical Learning

Description

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; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • 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. Front Matter ....Pages i-xxx Introduction (Ke-Lin Du, M. N. S. Swamy)....Pages 1-19 Fundamentals of Machine Learning (Ke-Lin Du, M. N. S. Swamy)....Pages 21-63 Elements of Computational Learning Theory (Ke-Lin Du, M. N. S. Swamy)....Pages 65-79 Perceptrons (Ke-Lin Du, M. N. S. Swamy)....Pages 81-95 Multilayer Perceptrons: Architecture and Error Backpropagation (Ke-Lin Du, M. N. S. Swamy)....Pages 97-141 Multilayer Perceptrons: Other Learing Techniques (Ke-Lin Du, M. N. S. Swamy)....Pages 143-172 Hopfield Networks, Simulated Annealing, and Chaotic Neural Networks (Ke-Lin Du, M. N. S. Swamy)....Pages 173-200 Associative Memory Networks (Ke-Lin Du, M. N. S. Swamy)....Pages 201-229 Clustering I: Basic Clustering Models and Algorithms (Ke-Lin Du, M. N. S. Swamy)....Pages 231-274 Clustering II: Topics in Clustering (Ke-Lin Du, M. N. S. Swamy)....Pages 275-314 Radial Basis Function Networks (Ke-Lin Du, M. N. S. Swamy)....Pages 315-349 Recurrent Neural Networks (Ke-Lin Du, M. N. S. Swamy)....Pages 351-371 Principal Component Analysis (Ke-Lin Du, M. N. S. Swamy)....Pages 373-425 Nonnegative Matrix Factorization (Ke-Lin Du, M. N. S. Swamy)....Pages 427-445 Independent Component Analysis (Ke-Lin Du, M. N. S. Swamy)....Pages 447-482 Discriminant Analysis (Ke-Lin Du, M. N. S. Swamy)....Pages 483-501 Reinforcement Learning (Ke-Lin Du, M. N. S. Swamy)....Pages 503-523 Compressed Sensing and Dictionary Learning (Ke-Lin Du, M. N. S. Swamy)....Pages 525-547 Matrix Completion (Ke-Lin Du, M. N. S. Swamy)....Pages 549-568 Kernel Methods (Ke-Lin Du, M. N. S. Swamy)....Pages 569-592 Support Vector Machines (Ke-Lin Du, M. N. S. Swamy)....Pages 593-644 Probabilistic and Bayesian Networks (Ke-Lin Du, M. N. S. Swamy)....Pages 645-698 Boltzmann Machines (Ke-Lin Du, M. N. S. Swamy)....Pages 699-715 Deep Learning (Ke-Lin Du, M. N. S. Swamy)....Pages 717-736 Combining Multiple Learners: Data Fusion and Ensemble Learning (Ke-Lin Du, M. N. S. Swamy)....Pages 737-767 Introduction to Fuzzy Sets and Logic (Ke-Lin Du, M. N. S. Swamy)....Pages 769-801 Neurofuzzy Systems (Ke-Lin Du, M. N. S. Swamy)....Pages 803-828 Neural Network Circuits and Parallel Implementations (Ke-Lin Du, M. N. S. Swamy)....Pages 829-851 Pattern Recognition for Biometrics and Bioinformatics (Ke-Lin Du, M. N. S. Swamy)....Pages 853-870 Data Mining (Ke-Lin Du, M. N. S. Swamy)....Pages 871-903 Big Data, Cloud Computing, and Internet of Things (Ke-Lin Du, M. N. S. Swamy)....Pages 905-932 Back Matter ....Pages 933-988