Knowledge Discovery with Support Vector Machines: 3 (Wiley Series on Methods and Applications in Data Mining) - Hardcover

Hamel, Lutz H.

Hardcover

ISBN 10: 0470371927 ISBN 13: 9780470371923

Publisher: Wiley-Interscience, 2009

Synopsis

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.

About this title

About the Author

Lutz Hamel, PhD, teaches at the University of Rhode Island, where he founded the machine learning and data mining group. His major research interests are computational logic, machine learning, evolutionary computation, data mining, bioinformatics, and computational structures in art and literature.

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.

Edition details

• Publisher: Wiley-Interscience
• Publication date: 2009
• Language: English
• ISBN 10: 0470371927
• ISBN 13: 9780470371923
• Binding: Hardcover
• Edition number: 1
• Number of pages: 264