The purpose of the Summer 1992 workshop, held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute, was to redress the lack of communication among the many different fields that address supervised learning. The first part of the volume contains chapters that have an almost purely theoretical bent. The second part contains chapters that are more directly concerned with real-world applications. Among the topics: statistical physics models of supervised learning; a study of maximal-coverage learning algorithms; on Bayesian model selection; and image segmentation and recognition. Annotation copyright Book News, Inc. Portland, Or.
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This volume grew out of a workshop designed to bring together researchers from different fields and includes contributions from workers in Bayesian analysis, machine learning, neural nets, PAC and VC theory, classical sampling theory statistics and the statistical physics of learning. The contributions present a bird's-eye view of the subject.
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