The Design and Analysis of Efficient Learning Algorithms (Acm Doctoral Dissertation Awards) von Schapire, Robert E.: Fine hardcover (1992)

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Bestandsnummer des Verk�ufers MIT-HC-LN-0262193256

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Bibliografische Details

Titel: The Design and Analysis of Efficient Learning Algorithms (Acm Doctoral Dissertation Awards)
Verlag: Mit Pr
Erscheinungsjahr: 1992
Sprache: Englisch
ISBN-10: 0262193256
ISBN-13: 9780262193252
Einband: hardcover
Zustand: Fine

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Inhaltsangabe

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.

Rese�a del editor

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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