An easy-to-follow introduction to support vector machinesThis 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 environmentsDescribing data mathematicallyLinear decision surfaces and functionsPerceptron learningMaximum margin classifiersSupport vector machinesElements of statistical learning theoryMulti-class classificationRegression with support vector machinesNovelty detectionComplemented 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.

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