Repairing self-confident active-transductive learners using systematic exploration

Authors:
Ron Begleiter;Ran El-Yaniv;Dmitry Pechyony
Affiliations:
Computer Science Department, Technion Israel Institute of Technology, Haifa 32000, Israel;Computer Science Department, Technion Israel Institute of Technology, Haifa 32000, Israel;Computer Science Department, Technion Israel Institute of Technology, Haifa 32000, Israel
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

We consider an active learning game within a transductive learning model. A major problem with many active learning algorithms is that an unreliable current hypothesis can mislead the querying component to query ''uninformative'' points. In this work we propose a remedy to this problem. Our solution can be viewed as a ''patch'' for fixing this deficiency and also as a proposed modular approach for active-transductive learning that produces powerful new algorithms. Extensive experiments on ''real'' data demonstrate the advantage of our method.