Abstract

In this chapter, we describe how the p-values derived from the conformal predictions framework can be used for active learning; that is, to select the informative examples from a data collection that can be used to train a classifier for best performance. We show the connection of this approach to information-theoretic methods, as well as show how the methodology can be generalized to multiple classifier models and information fusion settings.

Original languageEnglish (US)
Title of host publicationConformal Prediction for Reliable Machine Learning
Subtitle of host publicationTheory, Adaptations and Applications
PublisherElsevier Inc.
Pages49-70
Number of pages22
ISBN (Print)9780123985378
DOIs
StatePublished - Apr 2014

Keywords

  • Active Learning
  • Face Recognition
  • Image Classification
  • Multicriteria Active Learning
  • Online Active Learning
  • Query by Transduction

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Balasubramanian, V. N., Chakraborty, S., Ho, S. S., Wechsler, H., & Panchanathan, S. (2014). Active Learning. In Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications (pp. 49-70). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-398537-8.00003-1