Machine Learning With SVM And Other Kernal Methods by K. P. Soman, R. Loganathan, V. Ajay
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
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:
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