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Neural Networks and Statistical Learning

  • Ke-Lin Du
  • M. N. S. Swamy
Textbook

Table of contents

  1. Front Matter
    Pages i-xxx
  2. Ke-Lin Du, M. N. S. Swamy
    Pages 1-19
  3. Ke-Lin Du, M. N. S. Swamy
    Pages 21-63
  4. Ke-Lin Du, M. N. S. Swamy
    Pages 65-79
  5. Ke-Lin Du, M. N. S. Swamy
    Pages 81-95
  6. Ke-Lin Du, M. N. S. Swamy
    Pages 143-172
  7. Ke-Lin Du, M. N. S. Swamy
    Pages 201-229
  8. Ke-Lin Du, M. N. S. Swamy
    Pages 231-274
  9. Ke-Lin Du, M. N. S. Swamy
    Pages 275-314
  10. Ke-Lin Du, M. N. S. Swamy
    Pages 315-349
  11. Ke-Lin Du, M. N. S. Swamy
    Pages 351-371
  12. Ke-Lin Du, M. N. S. Swamy
    Pages 373-425
  13. Ke-Lin Du, M. N. S. Swamy
    Pages 427-445
  14. Ke-Lin Du, M. N. S. Swamy
    Pages 447-482
  15. Ke-Lin Du, M. N. S. Swamy
    Pages 483-501
  16. Ke-Lin Du, M. N. S. Swamy
    Pages 503-523
  17. Ke-Lin Du, M. N. S. Swamy
    Pages 525-547
  18. Ke-Lin Du, M. N. S. Swamy
    Pages 549-568
  19. Ke-Lin Du, M. N. S. Swamy
    Pages 569-592
  20. Ke-Lin Du, M. N. S. Swamy
    Pages 593-644
  21. Ke-Lin Du, M. N. S. Swamy
    Pages 645-698
  22. Ke-Lin Du, M. N. S. Swamy
    Pages 699-715
  23. Ke-Lin Du, M. N. S. Swamy
    Pages 717-736
  24. Ke-Lin Du, M. N. S. Swamy
    Pages 737-767
  25. Ke-Lin Du, M. N. S. Swamy
    Pages 769-801
  26. Ke-Lin Du, M. N. S. Swamy
    Pages 803-828
  27. Ke-Lin Du, M. N. S. Swamy
    Pages 829-851
  28. Ke-Lin Du, M. N. S. Swamy
    Pages 853-870
  29. Ke-Lin Du, M. N. S. Swamy
    Pages 871-903
  30. Ke-Lin Du, M. N. S. Swamy
    Pages 905-932
  31. Back Matter
    Pages 933-988

About this book

Introduction

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.

Keywords

Data Mining, Data Fusion and Ensemble Learning Multilayer Perceptrons Neural Networks Pattern Recognition Statistical and Machine Learning Neural Networks Textbook Statistical Learning Textbook Neural Networking Techniques Spar Coding Deep Learning Big Data Cloud Computing

Authors and affiliations

  • Ke-Lin Du
    • 1
  • M. N. S. Swamy
    • 2
  1. 1.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

Bibliographic information