The design and analysis of efficient learning algorithms

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Book Description:

Approaches to machine building 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 the results derived from the mathematics-oriented framework of computational learning theory. By focusing on the design of efficient learning algorithms and their performance, he develops a solid theoretical foundation for the study and understanding of machine learning. Since many of the findings address the fundamental problem of learning a concept from examples, Schapire begins with a brief introduction to the Valiant model, which generated much of the research on this problem. So four separate chapters consider different aspects of machine learning. His contributions include a general technique for drastically improving the error rate of a “weak” learning algorithm which can also be used to improve the spatial efficiency of many known learning algorithms; a detailed exploration of a powerful statistical method to efficiently deduce the structure of certain types of Boolean formulas from random examples of the formula’s input and output behavior; extending a standard concept learning model 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 environment); and a description of the algorithms a robot can use to infer the “structure” of its environment through experimentation. Robert E. Schapire received his doctorate from the Massachusetts Institute of Technology. He is now a member of the Research Department on Artificial Intelligence Principles at AT&T Bell Laboratories.

 

The design and analysis of efficient learning algorithms

Author(s): Robert E. Schapire

Series: ACM doctoral dissertation awards 1991

Publisher: MIT Press, Year: 1992

ISBN: 0262193256,9780262193252


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