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

Sholom M. Weiss et Casimir A. Kulikowski

ISBN 10: 1558600655 / ISBN 13: 9781558600652

Edité par Morgan Kaufmann Publishers In, 1990

A propos de cet article

Ancien livre de bibliothèque. Traces d'usure sur la couverture. Edition 1990. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Edition 1990. Ammareal gives back up to 15% of this item's net price to charity organizations.

N° de réf. du vendeur E-594-414

Détails bibliographiques

• Titre: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems
• Éditeur: Morgan Kaufmann Publishers In
• Date de parution: 1990
• Langue: anglais
• ISBN à 10 chiffres: 1558600655
• ISBN à 13 chiffres: 9781558600652
• Reliure: Hardcover
• État de l'article: Bon

A propos de ce titre

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.

À propos des auteurs

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.

Dr. Kulikowsky is Board of Governors Professor of Computer Science at Rutgers University. He is a member of the US National Academy of Medicine, and a Fellow of the AAAI, AAAS, AIMBE, ACMI, IEEE, IAHSI, IMIA. He has worked in the field of AI since the 1960s, being a pioneer in expert systems and machine learning.