Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

David Haussler; Michael Kearns; Manfred Opper; Robert Schapire

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

David Haussler
, Michael Kearns
, Manfred Opper
, Robert Schapire

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 4, NIPS 1991
Editors	John E. Moody, Stephen Jose Hanson, Richard Lippmann
Publisher	Neural information processing systems foundation
Pages	855-862
Number of pages	8
ISBN (Electronic)	1558602224, 9781558602229
State	Published - 1991
Externally published	Yes
Event	4th Advances in Neural Information Processing Systems, NIPS 1991 - Denver, United States Duration: Dec 2 1991 → Dec 5 1991

Publication series

Name	Advances in Neural Information Processing Systems
Volume	4
ISSN (Print)	1049-5258

Conference

Conference	4th Advances in Neural Information Processing Systems, NIPS 1991
Country/Territory	United States
City	Denver
Period	12/2/91 → 12/5/91

All Science Journal Classification (ASJC) codes

Signal Processing
Information Systems
Computer Networks and Communications

Cite this

Haussler, D., Kearns, M., Opper, M., & Schapire, R. (1991). Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. In J. E. Moody, S. J. Hanson, & R. Lippmann (Eds.), Advances in Neural Information Processing Systems 4, NIPS 1991 (pp. 855-862). (Advances in Neural Information Processing Systems; Vol. 4). Neural information processing systems foundation.

Haussler, David ; Kearns, Michael ; Opper, Manfred et al. / Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. Advances in Neural Information Processing Systems 4, NIPS 1991. editor / John E. Moody ; Stephen Jose Hanson ; Richard Lippmann. Neural information processing systems foundation, 1991. pp. 855-862 (Advances in Neural Information Processing Systems).

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title = "Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods",

abstract = "In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.",

author = "David Haussler and Michael Kearns and Manfred Opper and Robert Schapire",

note = "Publisher Copyright: {\textcopyright} 1991 Neural information processing systems foundation. All rights reserved.; 4th Advances in Neural Information Processing Systems, NIPS 1991 ; Conference date: 02-12-1991 Through 05-12-1991",

year = "1991",

language = "English (US)",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

pages = "855--862",

editor = "Moody, \{John E.\} and Hanson, \{Stephen Jose\} and Richard Lippmann",

booktitle = "Advances in Neural Information Processing Systems 4, NIPS 1991",

}

Haussler, D, Kearns, M, Opper, M & Schapire, R 1991, Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. in JE Moody, SJ Hanson & R Lippmann (eds), Advances in Neural Information Processing Systems 4, NIPS 1991. Advances in Neural Information Processing Systems, vol. 4, Neural information processing systems foundation, pp. 855-862, 4th Advances in Neural Information Processing Systems, NIPS 1991, Denver, United States, 12/2/91.

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. / Haussler, David; Kearns, Michael; Opper, Manfred et al.
Advances in Neural Information Processing Systems 4, NIPS 1991. ed. / John E. Moody; Stephen Jose Hanson; Richard Lippmann. Neural information processing systems foundation, 1991. p. 855-862 (Advances in Neural Information Processing Systems; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

AU - Haussler, David

AU - Kearns, Michael

AU - Opper, Manfred

AU - Schapire, Robert

PY - 1991

Y1 - 1991

N2 - In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.

AB - In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.

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UR - https://www.scopus.com/pages/publications/105021095207#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:105021095207

T3 - Advances in Neural Information Processing Systems

SP - 855

EP - 862

BT - Advances in Neural Information Processing Systems 4, NIPS 1991

A2 - Moody, John E.

A2 - Hanson, Stephen Jose

A2 - Lippmann, Richard

PB - Neural information processing systems foundation

T2 - 4th Advances in Neural Information Processing Systems, NIPS 1991

Y2 - 2 December 1991 through 5 December 1991

ER -

Haussler D, Kearns M, Opper M, Schapire R. Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. In Moody JE, Hanson SJ, Lippmann R, editors, Advances in Neural Information Processing Systems 4, NIPS 1991. Neural information processing systems foundation. 1991. p. 855-862. (Advances in Neural Information Processing Systems).

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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