Machine Learning with SVM and Other Kernal Methods Engelska 2009

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. This title includes 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 t