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Design and Analysis of Efficient Learning Algorithms (ACM Doctoral Dissertation Award) - Hardcover

 
9780262193252: Design and Analysis of Efficient Learning Algorithms (ACM Doctoral Dissertation Award)

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Approaches to building machines that can learn from experience abound - from connectionist learning algorithms and genetic algorithms to statistical mechanics and a learning system based on Piaget's theories of early childhood development. This monograph describes results derived from the mathematically oriented framework of computational learning theory. Focusing on the design of efficient learning algorithms and their performance, it develops a sound, theoretical foundation for studying and understanding machine learning. Since many results concern the fundamental problem of learning a concept from examples, Schapire begins with a brief introduction to the Valiant model, which has generated much of the research on this problem. Four self-contained chapters then consider different aspects of machine learning. Their contributions include a general technique for dramatically improving the error rate of a "weak" learning algorithm that can also be used to improve the space efficiency of many known learning algorithms; a detailed exploration of a powerful statistical method for efficiently inferring the structure of certain kinds of Boolean formulas from random examples of the formula's input-output behaviour; the extension of a standard model of concept learning to accommodate concepts that exhibit uncertain or probabilistic behaviour; (including a variety of tools and techniques for designing efficient learning algorithms in such a probabilistic setting); and a description of algorithms that can be used by a robot to infer the "structure" of its environment through experimentation.

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Approaches to building machines that can learn from experience abound - from connectionist learning algorithms and genetic algorithms to statistical mechanics and a learning system based on Piaget's theories of early childhood development. This monograph describes results derived from the mathematically oriented framework of computational learning theory. Focusing on the design of efficient learning algorithms and their performance, it develops a sound, theoretical foundation for studying and understanding machine learning. Since many results concern the fundamental problem of learning a concept from examples, Schapire begins with a brief introduction to the Valiant model, which has generated much of the research on this problem. Four self-contained chapters then consider different aspects of machine learning. Their contributions include a general technique for dramatically improving the error rate of a "weak" learning algorithm that can also be used to improve the space efficiency of many known learning algorithms; a detailed exploration of a powerful statistical method for efficiently inferring the structure of certain kinds of Boolean formulas from random examples of the formula's input-output behaviour; the extension of a standard model of concept learning to accommodate concepts that exhibit uncertain or probabilistic behaviour; (including a variety of tools and techniques for designing efficient learning algorithms in such a probabilistic setting); and a description of algorithms that can be used by a robot to infer the "structure" of its environment through experimentation.

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Schapire, Robert E.
Verlag: The MIT Press, 1992
ISBN 10: 0262193256 ISBN 13: 9780262193252
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Hardcover. Zustand: As New. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages. Bestandsnummer des Verkäufers MIT-HC-LN-0262193256

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Robert E Schapire
Verlag: MIT Press, 1992
ISBN 10: 0262193256 ISBN 13: 9780262193252
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Hardcover. Zustand: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Couverture différente. Edition 1992. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Different cover. Edition 1992. Ammareal gives back up to 15% of this item's net price to charity organizations. Bestandsnummer des Verkäufers E-592-911

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