Support Vector Machine Introduction (Series of International Computer Science Textbooks)

Author: Christianini
Publisher:
Publish Date: 2004-03-01
Features: Support Vector Machines (SVM) are a new generation of learning algorithms developed on the basis of statistical learning theory. They have been successfully applied in fields such as text classification, handwritten recognition, image classification, and bioinformatics. This book is a comprehensive introduction to Support Vector Machines (SVM). It starts with the fundamental issues of machine learning algorithms and introduces the relevant background knowledge in a step-by-step manner, including linear classifiers, kernel function feature spaces, generalization theory, and optimization theory. On this basis, the algorithm of Support Vector Machines is naturally introduced. At the end of the book, a detailed discussion is provided on a series of important applications of Support Vector Machines and their implementation techniques. The book is written with clear and rigorous exposition, strong self-containedness, and the extensive references and website links provided serve as an ideal starting point for further study. This book can be used as a graduate textbook for computer science, automation, mechatronics engineering, applied mathematics, and other related fields, as a reference textbook for courses in neural networks, machine learning, data mining, artificial intelligence, and other disciplines, and as a reference book for teachers and researchers in related fields.