An easy-to-follow introduction to support vector machines 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.