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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Morgan Kaufmann | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (298 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: A textbook suitable for undergraduate courses in machine learning and related topics, this book provides a broad survey of the field. Generous exercises and examples give students a firm grasp of the concepts and techniques of this rapidly developing, challenging subject. Introduction to Machine Learning synthesizes and clarifies the work of leading researchers, much of which is otherwise available only in undigested technical reports, journals, and conference proceedings. Beginning with an overview suitable for undergraduate readers, Kodratoff establishes a theoretical basis for machine learning and describes its technical concepts and major application areas. Relevant logic programming examples are given in Prolog. Introduction to Machine Learning is an accessible and original introduction to a significant research area.
    Note: Online resource; Title from title page (viewed June 28, 2014) , Mode of access: World Wide Web.
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Morgan Kaufmann | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (825 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: 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.
    Note: Online resource; Title from title page (viewed June 28, 2014) , Mode of access: World Wide Web.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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