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A framework for structural risk minimisation

Authors:

John Shawe-Taylor,

Peter L. Bartlett,

Robert C. Williamson,

Martin AnthonyAuthors Info & Claims

COLT '96: Proceedings of the ninth annual conference on Computational learning theory

Pages 68 - 76

https://doi.org/10.1145/238061.238070

Published: 01 January 1996 Publication History

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References

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N. Alon, S. Ben-David, N. Cesa-Bianchi, D. Haussler, "Scale-sensitive Dimensions, Uniform Convergence, and Learnability," in Proceedings of the Conference on Foundations of Computer Science (FOCS), 1993. Also to appear in Journal of the ACM.

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Guangli XZhihao ZShuo XZiwei LLiangxue C(2024)Intelligent identification method for pipeline ultrasonic internal inspectionNondestructive Testing and Evaluation10.1080/10589759.2024.2389935(1-22)Online publication date: 12-Aug-2024
https://doi.org/10.1080/10589759.2024.2389935
Holland MWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)PAC-Bayes under potentially heavy tailsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454531(2715-2724)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454531
Verma VLamb AKannala JBengio YLopez-Paz D(2019)Interpolation consistency training for semi-supervised learningProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367546(3635-3641)Online publication date: 10-Aug-2019
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Index Terms

A framework for structural risk minimisation
1. Computing methodologies
  1. Machine learning
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
2. Theory of computation
  1. Computational complexity and cryptography
    1. Complexity classes
  2. Design and analysis of algorithms

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Published In

COLT '96: Proceedings of the ninth annual conference on Computational learning theory

January 1996

344 pages

ISBN:0897918118

DOI:10.1145/238061

Chairmen:
Avrim Blum
Carnegie Mellon Univ., Pittsburgh, PA
,
Michael Kearns
AT&T Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 1996

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9COLT96

Sponsor:

SIGAI
SIGACT
Univ degli Studi de Milano

9COLT96: 9th Annual Conference on Computational Learning Theory

June 28 - July 1, 1996

Desenzano del Garda, Italy

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Overall Acceptance Rate 35 of 71 submissions, 49%

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Cited By

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Guangli XZhihao ZShuo XZiwei LLiangxue C(2024)Intelligent identification method for pipeline ultrasonic internal inspectionNondestructive Testing and Evaluation10.1080/10589759.2024.2389935(1-22)Online publication date: 12-Aug-2024
https://doi.org/10.1080/10589759.2024.2389935
Holland MWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)PAC-Bayes under potentially heavy tailsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454531(2715-2724)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454531
Verma VLamb AKannala JBengio YLopez-Paz D(2019)Interpolation consistency training for semi-supervised learningProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367546(3635-3641)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367546
Kim M(2019)Large-margin learning of Cox proportional hazard models for survival analysisApplied Intelligence10.1007/s10489-018-1363-349:5(1675-1687)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10489-018-1363-3
Jayadeva Chandra SBatra SSabharwal S(2016)Learning a hyperplane regressor through a tight bound on the VC dimensionNeurocomputing10.1016/j.neucom.2015.06.065171:C(1610-1616)Online publication date: 1-Jan-2016
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Jayadeva (2015)Learning a hyperplane classifier by minimizing an exact bound on the VC dimension1Neurocomputing10.1016/j.neucom.2014.07.062149:PB(683-689)Online publication date: 3-Feb-2015
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Li LHu QWu XYu D(2014)Exploration of classification confidence in ensemble learningPattern Recognition10.1016/j.patcog.2014.03.02147:9(3120-3131)Online publication date: Sep-2014
https://doi.org/10.1016/j.patcog.2014.03.021
Hu QLi LWu XSchaefer GYu D(2014)Exploiting diversity for optimizing margin distribution in ensemble learningKnowledge-Based Systems10.1016/j.knosys.2014.06.00567(90-104)Online publication date: 1-Sep-2014
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Kim M(2013)Semi-supervised learning of hidden conditional random fields for time-series classificationNeurocomputing10.1016/j.neucom.2013.03.024119(339-349)Online publication date: 1-Nov-2013
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