Machine Learning Proceedings 1988 PDF Download

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: 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: Antoni Kreczmar
Publisher: Springer Science & Business Media
ISBN: 9783540523253
Category : Computers
Languages : en
Pages : 150

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Book Description
LOGLAN '88 belongs to the family of object oriented programming languages. It embraces all important known tools and characteristics of OOP, i.e. classes, objects, inheritance, coroutine sequencing, but it does not get rid of traditional imperative programming: primitive types do not need to be objects; records, static arrays, subtypes and other similar type contructs are admitted. LOGLAN has non-traditional memory model which accepts programmed deallocation but avoids dangling reference. The LOGLAN semantic model provides multi-level inheritance, which properly cooperates with module nesting. Parallelism in LOGLAN has an object oriented nature. Processes are treated like objects of classes and communication between processes is provided by alien calls similar to remote calls.

Author: Ryszard S. Michalski
Publisher: Springer Science & Business Media
ISBN: 1461532027
Category : Computers
Languages : en
Pages : 156

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Book Description
Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.