Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems - Hardcover

Weiss, Sholom M.; Kulikowski, Casimir A.

Hardcover

ISBN 10: 1558600655 ISBN 13: 9781558600652

Publisher: Morgan Kaufmann, 1990

Synopsis

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.

Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.

The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

About this title

About the Author

Sholom M. Weiss is a professor of computer science at Rutgers University and the author of dozens of research papers on data mining and knowledge-based systems. He is a fellow of the American Association for Artificial Intelligence, serves on numerous editorial boards of scientific journals, and has consulted widely on the commercial application of advanced data mining techniques. He is the author, with Casimir Kulikowski, of Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, which is also available from Morgan Kaufmann Publishers.

From the Back Cover

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.

Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.

The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 1990
• Language: English
• ISBN 10: 1558600655
• ISBN 13: 9781558600652
• Binding: Hardcover
• Edition number: 1
• Number of pages: 223