Machine Learning: An Artificial Intelligence Approach, Volume II

Ryszard S. Michalski, Jaime G. Carbonell et Tom M. Mitchell

ISBN 10: 0934613001 / ISBN 13: 9780934613002

Published by Morgan Kaufmann, 1986

About this Item

Ancien livre de bibliothèque avec équipements. Edition 1986. Tome 2. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 1986. Volume 2. Ammareal gives back up to 15% of this item's net price to charity organizations. Seller Inventory # G-128-637

Bibliographic Details

Title: Machine Learning: An Artificial Intelligence...

Publisher: Morgan Kaufmann

Publication Date: 1986

Binding: Hardcover

Condition: Bon

About this title

Synopsis:

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs―particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems―one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS).
This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

From the Back Cover:

Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

See below for earlier volumes in the series.