The Design and Analysis of Efficient Learning Algorithms

The Design and Analysis of Efficient Learning Algorithms By Robert E. Schapire
1992 | 231 Pages | ISBN: 0262193256 | DJVU | 2 MB

Approaches to building machines that can learn from experience abound â€" 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 of the 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 behavior; the extension of a standard model of concept learning to accommodate concepts that exhibit uncertain or probabilistic behavior; (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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