Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems
Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems by Sholom M. Weiss is published by Morgan Kaufmann and was released on October 15, 1990. This book serves as a practical guide to classification learning systems and their applications, detailing how computer programs can learn from sample data to make predictions for new cases, sometimes outperforming human capabilities.
Readers will find a comprehensive examination of practical learning systems derived from statistical pattern recognition, neural networks, and machine learning. The author employs an engineering approach, providing intuitive explanations with minimal mathematics to ensure accessibility for a wide audience. The book discusses the strengths and weaknesses of various learning methods, including linear discriminants, back-propagation neural networks, and decision trees, while also contrasting these systems with rule-based counterparts from expert systems. With 223 pages, this edition is designed to equip practitioners with the knowledge to select and apply effective learning methods.
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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.
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