Abstract

Transductive inference has gained popularity in recent years as a means to develop pattern classification approaches that address the specific issue of predicting the class label of a given data point, instead of the more general problem of inferring the ideal classifier function. In this paper, we propose a Generalized Query by Transduction (GQBT) approach for active learning in the online setting. This approach is based on the theory of conformal predictions, which has recently been proposed based on principles of algorithmic randomness, transductive inference and hypothesis testing. The proposed GQBT approach can be used along with any existing pattern classification algorithm, and can also be used to combine multiple criteria in selecting an unlabeled example appropriately in the active learning process. The results of our experiments with different datasets from the UCI Machine Learning repository demonstrate high promise in the proposed approach, with significantly lower label complexities than other existing online active learning approaches. The GQBT approach was also evaluated on face recognition using videos from the VidTIMIT dataset, and the observed superior performance supports the potential of applicability of the proposed approach in real-world problems.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages1378-1385
Number of pages8
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: Sep 27 2009Oct 4 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Other

Other2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
CountryJapan
CityKyoto
Period9/27/0910/4/09

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Balasubramanian, V., Chakraborty, S., & Panchanathan, S. (2009). Generalized query by transduction for online active learning. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 (pp. 1378-1385). [5457449] (2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009). https://doi.org/10.1109/ICCVW.2009.5457449