Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems (Machine Learning Series) von Kulikowski, Casimir A.,Weiss, Sholom M.: Good Hardcover (1990)

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Bibliografische Details

Titel: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems (Machine Learning Series)
Verlag: Morgan Kaufmann
Erscheinungsjahr: 1990
Sprache: Englisch
ISBN-10: 1558600655
ISBN-13: 9781558600652
Einband: Hardcover
Zustand: Good

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Inhaltsangabe

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. "I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and data miners." Emanuel Parzen Distinguished Professor of Statistics, Texas A&M University

Rese�a del editor

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. "I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and data miners." Emanuel Parzen Distinguished Professor of Statistics, Texas A&M University

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