This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover:
Knowledge discovery environments
Describing data mathematically
Linear decision surfaces and functions
Perceptron learning
Maximum margin classifiers
Support vector machines
Elements of statistical learning theory
Multi-class classification
Regression with support vector machines
Novelty detection
Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.