Machine Learning With Svm And Other Kernel Methods

Machine Learning with Svm and Other Kernel Methods

Machine Learning with SVM and Other Kernel Methods

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. KEY FEATURES  Extensive coverage of Lagrangian duality and iterative methods for optimization  Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing  A chapter on latest sequential minimization algorithms and its modifications to do online learning  Step-by-step method of solving the SVM based classification problem in Excel.  Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.