On transductive support vector machines

On transductive support vector machines (2007)

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by Junhui Wang

Venue:	Prediction and Discovery
Citations:	6 - 3 self

BibTeX

@INPROCEEDINGS{Wang07ontransductive,
    author = {Junhui Wang},
    title = {On transductive support vector machines},
    booktitle = {Prediction and Discovery},
    year = {2007},
    publisher = {American Mathematical Society}
}

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Abstract

Transductive support vector machines (TSVM) has been widely used as a means of treating partially labeled data in semisupervised learning. Around it, there has been mystery because of lack of understanding its foundation in generalization. This article aims to clarify several controversial aspects regarding TSVM. Two main results are established. First, TSVM performs no worse than its supervised counterpart SVM when tuning is performed, which is contrary to several studies indicating otherwise. The “alleged ” inferior performance of TSVM is mainly because it was not tuned in the process, in addition to the involved minimization routines. Second, we utilize difference convex programming to derive a nonconvex minimization routine for TSVM, which compares favorably against some state-of-the-art methods. This, together with our learning theory lands some support to TSVM. 1

Citations

6694	Statistical Learning Theory - Vapnik - 1998
1491	Support-vector networks - Cortes, Vapnik - 1995
946	Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
632	Text classification from labeled and unlabeled documents using - Nigram, McCallum, et al.
509	Transductive inference for text classification using support vector machines - Joachims - 1999
325	Semi-supervised learning using gaussian fields and harmonic functions - Zhu, Ghahramani, et al.
230	New support vector algorithms - Schölkopf, Smola, et al. - 2000
202	Learning from Labeled and Unlabeled Data Using Graph Mincuts - Blum, Chawla
146	Semi-supervised support vector machines - Bennet, Demiriz - 1998
89	Semi-supervised classification by low density separation - Chapelle, Zien - 2005
72	A probability analysis on the value of unlabeled data for classification problems - Zhang, Oles - 2000
53	Convergence rate of sieve estimates - SHEN, H - 1994
38	Large scale transductive svms - Collobert, Sinz, et al.
35	Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data - Baluja - 1998
32	Information regularization with partially labeled data - Szummer, Jaakkola - 2003
13	Solving a class of linearly constrained indefinite quadratic problems by D.C. algorithms - An, Tao - 1997
13	Person identification in webcam images: an application of semi-supervised learning. ICML 2005 Workshop on Learning with Partially Classified Training Data - BALCAN, BLUM, et al. - 2005
11	On psi-learning - Shen, Tseng, et al. - 2003
9	Computational development of ψ-learning - Liu, Shen, et al. - 2005
6	Large margin decision trees for induction and transduction - Wu, Bennett, et al. - 1999
5	Semi-supervised learning with an explicit label-error model for misclassified data - AMINI, GALLINARI - 2003
4	Nonsmooh optimization techniques for semi-supervised classification (Technical Report). U. Pisa, Dipartimento di Matematica. www.dm.unipi.it/mat_dia_med/ PAPER_Astorino.pdf - Astorino, Fuduli - 2005
4	Discussion of 2004 IMS Medallion Lecture: “Local Rademacher complexities and oracle inequalities in risk minimization - Shen, Wang - 2006