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Learning by transduction

Authors:

A. Gammerman,

V. Vovk, and

V. VapnikAuthors Info & Claims

UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

July 1998

Pages 148 - 155

Published: 24 July 1998 Publication History

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Abstract

We describe a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i.d. process from a continuous probability distribution. Our method is a modification of Vapnik's support-vector machine; its main novelty is that it gives not only the prediction itself but also a practicable measure of the evidence found in support of that prediction. We also describe a procedure for assigning degrees of confidence to predictions made by the support vector machine. Some experimental results are presented, and possible extensions of the algorithms are discussed.

References

[1]

C. Cortes and V. Vapnik. Support-vector networks. Machine Learning 20:1-25, 1995.

Digital Library

Google Scholar

[2]

A. P. Dawid. Inference, statistical: I. In Encyclopedia of Statistical Sciences (S. Kotz and N. L. Johnson, Editors). Wiley, New York, 1983, vol. 4, pp. 89-105.

Google Scholar

[3]

D. A. S. Fraser. Sequentially determined statistically equivalent blocks. Ann. Math. Statist. 22:372, 1951.

Crossref

Google Scholar

[4]

A. Gammerman. Machine learning: progress and prospects. Technical Report CSD-TR-96-21, Department of Computer Science, Royal Holloway, University of London, December 1996.

Google Scholar

[5]

A. Gammerman and A. R. Thatcher. Bayesian diagnostic probabilities without assuming independence of symptoms. Yearbook of Medical Informatics, 1992, pp. 323-330.

Google Scholar

[6]

Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. J. Jackel. Handwritten digit recognition with backpropagation network. Advances in Neural Information Processing Systems 2. Morgan Kaufmann, 1990, pp. 396-404.

Digital Library

Google Scholar

[7]

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.

Digital Library

Google Scholar

[8]

V. G. Vovk. On the concept of the Bernoulli property. Russ. Math. Surv. 41:247-248, 1986.

Crossref

Google Scholar

[9]

V. G. Vovk. A logic of probability, with application to the foundations of statistics (with discussion). J. R. Statist. Soc. B 55:317-351, 1993.

Google Scholar

[10]

V. G. Vovk and V. V. V'yugin. On the empirical validity of the Bayesian method. J. R. Statist. Soc. B 55:253-266, 1993.

Google Scholar

Cited By

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Chalvidal MSerre TVanRullen ROh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Learning functional transductionProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669352(73852-73865)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669352
Park SDobriban ELee IBastani OKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)PAC prediction sets for meta-learningProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3603018(37920-37931)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3603018
Moreo AEsuli ASebastiani F(2021)Lost in Transduction: Transductive Transfer Learning in Text ClassificationACM Transactions on Knowledge Discovery from Data10.1145/345314616:1(1-21)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3453146
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Index Terms

Learning by transduction
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning

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Published In

UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

July 1998

538 pages

ISBN:155860555X

Editors:
Gregory F. Cooper
University of Pittsburgh, Pittsburgh, Pennsylvania
,
Serafín Moral
Universidad de Granada, Granada, Spain

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 24 July 1998

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Chalvidal MSerre TVanRullen ROh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Learning functional transductionProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669352(73852-73865)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669352
Park SDobriban ELee IBastani OKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)PAC prediction sets for meta-learningProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3603018(37920-37931)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3603018
Moreo AEsuli ASebastiani F(2021)Lost in Transduction: Transductive Transfer Learning in Text ClassificationACM Transactions on Knowledge Discovery from Data10.1145/345314616:1(1-21)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3453146
Sun YWang XLiu ZMiller JEfros AHardt MDaumé HSingh A(2020)Test-time training with self-supervision for generalization under distribution shiftsProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525794(9229-9248)Online publication date: 13-Jul-2020
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Chen JShah VKyrillidis ADaumé HSingh A(2020)Negative sampling in semi-supervised learningProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525097(1704-1714)Online publication date: 13-Jul-2020
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Berthelot DCarlini NGoodfellow IOliver APapernot NRaffel CWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)MixMatchProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454741(5049-5059)Online publication date: 8-Dec-2019
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Gong CYang JTao D(2019)Multi-Modal Curriculum Learning over GraphsACM Transactions on Intelligent Systems and Technology10.1145/332212210:4(1-25)Online publication date: 20-Jul-2019
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Iyer AJonnalagedda MParthasarathy SRadhakrishna ARajamani SMcKinley KFisher K(2019)Synthesis and machine learning for heterogeneous extractionProceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3314221.3322485(301-315)Online publication date: 8-Jun-2019
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Sivaraman AZhang TVan den Broeck GKim MAtlee JBultan TWhittle J(2019)Active inductive logic programming for code searchProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00044(292-303)Online publication date: 25-May-2019
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Johansson ULöfström TLinusson HBoström H(2019)Efficient Venn predictors using random forestsMachine Language10.1007/s10994-018-5753-x108:3(535-550)Online publication date: 1-Mar-2019
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Least Square Transduction Support Vector Machine

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