Introduction to Support Vector Machines

Author: Christianini, Li Guozheng, Wang Meng, Zeng Huajun
Publisher:
Publish Date: 2004-04-01
Features: "Introduction to Support Vector Machines" is a comprehensive book that introduces various standard techniques of support vector machines. It starts from learning methods, progresses to hyperplanes, kernel functions, generalization theory, and optimization theory, and concludes with a summary of support vector machine theory, while also introducing its implementation techniques and applications. The book is written in a progressive manner, with content that is both in-depth and easy to understand, earning the approval of many support vector machine researchers. Support Vector Machines (SVM) are a new generation of learning algorithms developed on the basis of statistical learning theory. They have achieved good applications in fields such as text classification, handwritten recognition, image classification, and bioinformatics. This book is a comprehensive introduction to Support Vector Machines (SVM). It begins with the fundamental issues of machine learning algorithms and introduces the relevant background knowledge in a progressive manner, including linear classifiers, kernel feature spaces, generalization theory, and optimization theory. On this basis, it naturally introduces the algorithm of support vector machines. At the end of the book, it also discusses in detail a series of important applications of support vector machines and their implementation techniques. The book is written clearly and rigorously, with strong self-containment, and the numerous references and website links provided can serve as an ideal starting point for further learning. This book can be used as a graduate textbook for computer science, automation, mechatronics engineering, applied mathematics, and other related fields. It can also serve as a reference textbook for courses such as neural networks, machine learning, data mining, and artificial intelligence, as well as a reference book for teachers and researchers in related fields.