Query by transduction.

Full Abstract

There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting.

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Author information

Author/s: Ho, Shen-Shyang (SS); Wechsler, Harry (H);

Affiliation: NASA Jet Propulsion Laboratory, Pasadena, CA 91109, USA. sho(-atsign-)jpl.nasa.gov

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2008-Sep; vol 30 (issue 9) : pp 1557-71

Dates: Created 2008/07/11; Completed 2008/09/23;

PMID: 18617715, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Information Storage and Retrieval - methods

Information Storage and Retrieval - methods
Pattern Recognition, Automated - methods
Sensitivity and Specificity

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