Volume 7 Number 6 (Jun. 2012)
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JSW 2012 Vol.7(6): 1307-1314 ISSN: 1796-217X
doi: 10.4304/jsw.7.6.1307-1314

Confidence Estimation for Graph-based Semi-supervised Learning

Tao Guo1 and Guiyang Li2
1Visual Computing and Visual Reality Key Laboratory of Sichuan Province, Chengdu, China
2College of Computer Science, Sichuan Normal University, Chengdu, China

Abstract—To select unlabeled example effectively and reduce classification error, confidence estimation for graphbased semi-supervised learning (CEGSL) is proposed. This algorithm combines graph-based semi-supervised learning with collaboration-training. It makes use of structure information of sample to calculate the classification probability of unlabeled example explicitly. With multi-classifiers, the algorithm computes the confidence of unlabeled example implicitly. With dualconfidence estimation, the unlabeled example is selected to update classifiers. The comparative experiments on UCI datasets indicate that CEGSL can effectively exploit unlabeled data to enhance the learning performance.

Index Terms—graph, collaboration-training, confidence, classification, semi-supervised leaning


Cite: Tao Guo and Guiyang Li, "Confidence Estimation for Graph-based Semi-supervised Learning," Journal of Software vol. 7, no. 6, pp. 1307-1314, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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