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Neural Networks and Statistical Learning

  • Ke-Lin Du
  • M. N. S. Swamy

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Ke-Lin Du, M. N. S. Swamy
    Pages 1-14
  3. Ke-Lin Du, M. N. S. Swamy
    Pages 15-65
  4. Ke-Lin Du, M. N. S. Swamy
    Pages 67-81
  5. Ke-Lin Du, M. N. S. Swamy
    Pages 127-157
  6. Ke-Lin Du, M. N. S. Swamy
    Pages 187-214
  7. Ke-Lin Du, M. N. S. Swamy
    Pages 215-258
  8. Ke-Lin Du, M. N. S. Swamy
    Pages 259-297
  9. Ke-Lin Du, M. N. S. Swamy
    Pages 299-335
  10. Ke-Lin Du, M. N. S. Swamy
    Pages 337-353
  11. Ke-Lin Du, M. N. S. Swamy
    Pages 355-405
  12. Ke-Lin Du, M. N. S. Swamy
    Pages 407-417
  13. Ke-Lin Du, M. N. S. Swamy
    Pages 419-450
  14. Ke-Lin Du, M. N. S. Swamy
    Pages 451-468
  15. Ke-Lin Du, M. N. S. Swamy
    Pages 469-524
  16. Ke-Lin Du, M. N. S. Swamy
    Pages 525-545
  17. Ke-Lin Du, M. N. S. Swamy
    Pages 547-561
  18. Ke-Lin Du, M. N. S. Swamy
    Pages 563-619
  19. Ke-Lin Du, M. N. S. Swamy
    Pages 621-643
  20. Ke-Lin Du, M. N. S. Swamy
    Pages 645-676
  21. Ke-Lin Du, M. N. S. Swamy
    Pages 677-704
  22. Ke-Lin Du, M. N. S. Swamy
    Pages 705-725
  23. Ke-Lin Du, M. N. S. Swamy
    Pages 727-745
  24. Ke-Lin Du, M. N. S. Swamy
    Pages 747-778
  25. Back Matter
    Pages 779-824

About this book

Introduction

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content.

Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence,

and data mining.

Keywords

Data Mining, Data Fusion and Ensemble Learning Multilayer Perceptrons Neural Networks Pattern Recognition Statistical and Machine Learning

Authors and affiliations

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

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-5571-3
  • Copyright Information Springer-Verlag London 2014
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-4471-5570-6
  • Online ISBN 978-1-4471-5571-3
  • Buy this book on publisher's site
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