Machine Learning Proceedings 1988 PDF Download
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Author: John Laird
Publisher: Morgan Kaufmann
ISBN: 1483297691
Category : Computers
Languages : en
Pages : 476
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Book Description
Machine Learning Proceedings 1988
Author: John Laird
Publisher: Morgan Kaufmann
ISBN: 1483297691
Category : Computers
Languages : en
Pages : 476
Get Book Here
Book Description
Machine Learning Proceedings 1988
Author: Russell Greiner
Publisher:
ISBN: 9781581138382
Category : Computer science
Languages : en
Pages : 942
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Book Description
Author: Peter Edwards
Publisher: Morgan Kaufmann
ISBN: 1483298531
Category : Computers
Languages : en
Pages : 497
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Book Description
Machine Learning Proceedings 1992
Author: Alberto Maria Segre
Publisher: Morgan Kaufmann
ISBN: 1483297403
Category : Computers
Languages : en
Pages : 521
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Book Description
Machine Learning Proceedings 1989
Author: Lawrence A. Birnbaum
Publisher: Morgan Kaufmann
ISBN: 1483298620
Category : Computers
Languages : en
Pages : 361
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Book Description
Machine Learning Proceedings 1993
Author: Lawrence A. Birnbaum
Publisher: Morgan Kaufmann
ISBN: 1483298175
Category : Computers
Languages : en
Pages : 682
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Book Description
Machine Learning
Author: Yves Kodratoff
Publisher: Elsevier
ISBN: 0080510558
Category : Computers
Languages : en
Pages : 836
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Book Description
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Author: Bruce Porter
Publisher: Morgan Kaufmann
ISBN: 1483298582
Category : Computers
Languages : en
Pages : 436
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Book Description
Machine Learning Proceedings 1990
Author: Jude W. Shavlik
Publisher: Morgan Kaufmann
ISBN: 9781558601437
Category : Computers
Languages : en
Pages : 868
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Book Description
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.
Author: Ryszard S. Michalski
Publisher: Morgan Kaufmann
ISBN: 9781558602519
Category : Computers
Languages : en
Pages : 798
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Book Description
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.
