Query by Transduction

Shen Shyang Ho, Harry Wechsler

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1557-1571
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume30
Issue number9
DOIs
StatePublished - Sep 1 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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