The mathematics of generalization : the proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning
著者
書誌事項
The mathematics of generalization : the proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning
（Proceedings volume in the Santa Fe Institute studies in the sciences of complexity, v. 20）
Addison-Wesley Pub. Co., c1995
- : hbk.
- : pbk.
大学図書館所蔵 全18件
注記
Workshop held Aug. 1992 in Santa Fe, N.M
Includes bibliographical references and index
内容説明・目次
- 巻冊次
-
: pbk. ISBN 9780201409833
内容説明
This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
目次
About the Santa Fe Institute -- Santa Fe Institute Studies in the Sciences of Complexity -- Preface -- The Status of Supervised Learning Science Circa 1994: The Search for a Consensus -- Reflections After Refereeing Papers for NIPS -- The Probably Approximately Correct (PAC) and Other Learning Models -- Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications -- The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework -- Statistical Physics Models of Supervised Learning -- On Exhaustive Learning -- A Study of Maximal-Coverage Learning Algorithms -- On Bayesian Model Selection -- Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance -- Current Research -- Preface to "Simplifying Neural Networks by Soft Weight Sharing" -- Simplifying Neural Networks by Soft Weight Sharing -- Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs -- Image Segmentation and Recognition
- 巻冊次
-
: hbk. ISBN 9780201409857
内容説明
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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