article

Learning with progressive transductive support vector machine

Authors:

Shihai DongAuthors Info & Claims

Pattern Recognition Letters, Volume 24, Issue 12

Pages 1845 - 1855

https://doi.org/10.1016/S0167-8655(03)00008-4

Published: 01 August 2003 Publication History

Abstract

Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the working set can be used as an additional source of information about margins. Compared with traditional inductive support vector machines, transductive support vector machine is often more powerful and can give better performance. In transduction, one estimates the classification function at points within the working set using information from both the training and the working set data. This will help to improve the generalization performance of SVMs, especially when training data is inadequate. Intuitively, we would expect transductive learning to yield improvements when the training sets are small or when there is a significant deviation between the training and working set subsamples of the total population. In this paper, a progressive transductive support vector machine is addressed to extend Joachims' transductive SVM to handle different class distributions. It solves the problem of having to estimate the ratio of positive/negative examples from the working set. The experimental results show the algorithm is very promising.

References

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Zheng XZhang LXu Z(2021)L1-norm Laplacian support vector machine for data reduction in semi-supervised learningNeural Computing and Applications10.1007/s00521-020-05609-935:17(12343-12360)Online publication date: 16-Jan-2021
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Razzaghi P(2019)Self-taught support vector machinesKnowledge and Information Systems10.1007/s10115-018-1218-659:3(685-709)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10115-018-1218-6
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Learning with progressive transductive support vector machine

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cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 24, Issue 12

August 2003

289 pages

ISSN:0167-8655

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2003

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Cited By

Zheng XZhang LXu Z(2021)L1-norm Laplacian support vector machine for data reduction in semi-supervised learningNeural Computing and Applications10.1007/s00521-020-05609-935:17(12343-12360)Online publication date: 16-Jan-2021
https://dl.acm.org/doi/10.1007/s00521-020-05609-9
Razzaghi P(2019)Self-taught support vector machinesKnowledge and Information Systems10.1007/s10115-018-1218-659:3(685-709)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10115-018-1218-6
Jia XRong X(2018)A Self-training Method for Detection of Phishing WebsitesData Mining and Big Data10.1007/978-3-319-93803-5_39(414-425)Online publication date: 17-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-93803-5_39
Xu YFang XWu JLi XZhang D(2016)Discriminative Transfer Subspace Learning via Low-Rank and Sparse RepresentationIEEE Transactions on Image Processing10.1109/TIP.2015.251049825:2(850-863)Online publication date: 1-Feb-2016
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Wang XWen JAlam SJiang ZWu Y(2016)Semi-supervised learning combining transductive support vector machine with active learningNeurocomputing10.1016/j.neucom.2015.08.087173:P3(1288-1298)Online publication date: 15-Jan-2016
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Leng YSun CXu XYuan QXing SWan HWang JLi D(2016)Employing unlabeled data to improve the classification performance of SVM, and its application in audio event classificationKnowledge-Based Systems10.1016/j.knosys.2016.01.02998:C(117-129)Online publication date: 15-Apr-2016
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Kan MWu JShan SChen X(2014)Domain Adaptation for Face RecognitionInternational Journal of Computer Vision10.1007/s11263-013-0693-1109:1-2(94-109)Online publication date: 1-Aug-2014
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Bhatia SMitra P(2012)Summarizing figures, tables, and algorithms in scientific publications to augment search resultsACM Transactions on Information Systems10.1145/2094072.209407530:1(1-24)Online publication date: 6-Mar-2012
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Selvaraj SBhar BSellamanickam SShevade S(2011)Semi-supervised SVMs for classification with unknown class proportions and a small labeled datasetProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063674(653-662)Online publication date: 24-Oct-2011
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Pang SBan TKadobayashi YKasabov N(2011)Personalized mode transductive spanning SVM classification treeInformation Sciences: an International Journal10.1016/j.ins.2011.01.008181:11(2071-2085)Online publication date: 1-Jun-2011
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