Machine Learning With SVM And Other Kernal Methods

By K. P. Soman, R. Loganathan, V. Ajay more
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Product Specifications

Publisher PHI Learning All Information Technology books by PHI Learning
ISBN 9788120334359
Author: K. P. Soman, R. Loganathan, V. Ajay
Number of Pages 497
Available
Available in all digital devices
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Machine Learning With SVM And Other Kernal Methods by K. P. Soman, R. Loganathan, V. Ajay
Book Summary:

Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory.

Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel.

Audience of the Book :
This book Useful for Computer Science, IT And MCA Student
Key Feature:

1. Extensive coverage of Lagrangian duality and iterative methods for optimization

2. Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing.

3. A chapter on latest sequential minimization algorithms and its modifications to do online learning

4. Step-by-step method of solving the SVM based classification problem in Excel.

5. Kernel versions of PCA, CCA and ICA

Table of Contents:

Preface

1. Machine Learning with Support Vector Machines

2. Supervised Automatic Learning: A Probabilistic Framework

3. Essential Mathematical Background

4. Kernel Methods and the Evolution of SVM

5. Support Vector Regression

6. Simple Variants of SVM: Mangasarian’s Approaches

7. Sequential Minimisation Algorithms

8. One Class SVM

9. Multi-class and Hierarchical Support Vector Machines

10. String Kernels

11. Kernel-based Methods for Clustering Data

12. Data Sets

13. Other Kernel Methods K-PCA, K-CCA, K-PLS, K-ICA

14. Kernel Methods for Text Categorisation.

15. Kernel Methods for Speech Recognition

16. Kernel Methods in Natural Language Processing: An Introduction

Appendix

Index

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