Article

Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise

Authors:

Manfred Opper,

David HausslerAuthors Info & Claims

COLT '91: Proceedings of the fourth annual workshop on Computational learning theory

Pages 75 - 87

Published: 15 August 1991 Publication History

Abstract

No abstract available.

Cited By

View all

Loog MViering TMey AWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Minimizers of the empirical risk and risk monotonicityProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454959(7478-7487)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454959
Ikeda KAoishi T(2005)An asymptotic statistical analysis of support vector machines with soft marginsNeural Networks10.1016/j.neunet.2004.11.00818:3(251-259)Online publication date: 1-Apr-2005
https://dl.acm.org/doi/10.1016/j.neunet.2004.11.008
Raudys ŠYoung D(2004)Results in statistical discriminant analysisJournal of Multivariate Analysis10.1016/S0047-259X(02)00021-089:1(1-35)Online publication date: 1-Apr-2004
https://dl.acm.org/doi/10.1016/S0047-259X%2802%2900021-0
Show More Cited By

Index Terms

Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Theory of computation
  1. Models of computation
    1. Probabilistic computation

Recommendations

Efficient learning of Naive Bayes classifiers under class-conditional classification noise
ICML '06: Proceedings of the 23rd international conference on Machine learning

We address the problem of efficiently learning Naive Bayes classifiers under class-conditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the distributions associated to each class are product distributions. When ...
Learning Naive Bayes Classifiers for Music Classification and Retrieval
ICPR '10: Proceedings of the 2010 20th International Conference on Pattern Recognition

In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector ...
CALCULATION OF THE LEARNING CURVE OF BAYES OPTIMAL CLASSIFICATION ALGORITHM FOR LEARNING A PERCEPTRON WITH NOISE

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

COLT '91: Proceedings of the fourth annual workshop on Computational learning theory

August 1991

383 pages

ISBN:1558602135

Editors:
Manfred K. Warmuth
Univ. of California, Santa Cruz
,
Leslie G. Valiant
Harvard Univ., Cambridge, MA

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 15 August 1991

Check for updates

Qualifiers

Article

Conference

COLT'91

Sponsor:

SIGAI
Office Naval Research
SIGACT

COLT'91: 4th Annual Workshop on Computational Learning Theory

August 5 - 7, 1991

California, Santa Cruz, USA

Acceptance Rates

Overall Acceptance Rate 35 of 71 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Loog MViering TMey AWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Minimizers of the empirical risk and risk monotonicityProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454959(7478-7487)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454959
Ikeda KAoishi T(2005)An asymptotic statistical analysis of support vector machines with soft marginsNeural Networks10.1016/j.neunet.2004.11.00818:3(251-259)Online publication date: 1-Apr-2005
https://dl.acm.org/doi/10.1016/j.neunet.2004.11.008
Raudys ŠYoung D(2004)Results in statistical discriminant analysisJournal of Multivariate Analysis10.1016/S0047-259X(02)00021-089:1(1-35)Online publication date: 1-Apr-2004
https://dl.acm.org/doi/10.1016/S0047-259X%2802%2900021-0
Ikeda K(2003)Generalization error analysis for polynomial kernel methodsProceedings of the 2003 joint international conference on Artificial neural networks and neural information processing10.5555/1767129.1767156(201-208)Online publication date: 26-Jun-2003
https://dl.acm.org/doi/10.5555/1767129.1767156
Servedio RBen-David SLong P(1999)On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithmProceedings of the twelfth annual conference on Computational learning theory10.1145/307400.307474(296-307)Online publication date: 6-Jul-1999
https://dl.acm.org/doi/10.1145/307400.307474
Wolpert DKnill EGrossman T(1998)Some results concerning off-training-set and IID error for the Gibbs and the Bayes optimal generalizersStatistics and Computing10.1023/A:10088670093128:1(35-54)Online publication date: 1-Jan-1998
https://dl.acm.org/doi/10.1023/A%3A1008867009312
Meir RFontanari JHaussler D(1992)On learning noisy threshold functions with finite precision weightsProceedings of the fifth annual workshop on Computational learning theory10.1145/130385.130416(280-286)Online publication date: 1-Jul-1992
https://dl.acm.org/doi/10.1145/130385.130416

Abstract

Cited By

Index Terms

Recommendations

Efficient learning of Naive Bayes classifiers under class-conditional classification noise

Learning Naive Bayes Classifiers for Music Classification and Retrieval

CALCULATION OF THE LEARNING CURVE OF BAYES OPTIMAL CLASSIFICATION ALGORITHM FOR LEARNING A PERCEPTRON WITH NOISE

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations