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.

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.

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.