Knowledge Discovery with Support Vector Machines

<b>An easy-to-follow introduction to support vector machines</b> <p>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:</p> <ul> <li> <p>Knowledge discovery environments</p> </li> <li> <p>Describing data mathematically</p> </li> <li> <p>Linear decision surfaces and functions</p> </li> <li> <p>Perceptron learning</p> </li> <li> <p>Maximum margin classifiers</p> </li> <li> <p>Support vector machines</p> </li> <li> <p>Elements of statistical learning theory</p> </li> <li> <p>Multi-class classification</p> </li> <li> <p>Regression with support vector machines</p> </li> <li> <p>Novelty detection</p> </li> </ul> <p>Complemented with hands-on exercises, algorithm descriptions, and data sets, <i>Knowledge Discovery with Support Vector Machines</i> 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.</p>