Neural Networks and Statistical Learning
Authors: Ke-Lin Du – M. N. S. Swamy
ISBN-10: 1447174518
ISBN-13: 9781447174516
Edition 版次: 2nd ed. 2019
Publication Date 出版日期: 2019-09-13
Print Length 页数: 988 pages
Book Description
By finelybook
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;
the 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.
1. Introduction
2. Fundamentals of Machine Learning
3. Elements of Computational Learning Theory
4. Perceptrons
5. Multilayer Perceptrons: Architecture and Error Backpropagation
6. Multilayer Perceptrons: Other Learing Techniques
7. Hopfheld Networks,Simulated Annealing,and Chaotic Neural Networks
8. Associative Memory Networks
9. Clustering: Basic Clustering Models and Algorithms
10. Clustering I: Topics in Clustering
11. Radial Basis Function Networks
12. Recurrent Neural Networks
13. Principal Component Analysis
14. Nonnegative Matrix Factorization
15. Independent Component Analysis
16. Discriminant Analysis
17. Reinforcement Learning
18. Compressed Sensing and Dictionary Learning
19. Matrix Completion
20. Kernel Methods
21. Support Vector Machines
22. Probabilistic and Bayesian Networks
23. Boltzmann Machines
24. Deep Learning
25. Combining Multiple Learners: Data Fusion and Ensemble Learning
26. Introduction to Fuzy Sets and Logic
27. Neurofuzzy Systems
28. Neural Network Circuits and Parallel Implementations
29. Pattern Recognition for Biometrics and Bioinformatics
30. Data Mining
31. Big Data,Cloud Computing,and Internet of Things
