Active Learning

Vineeth N. Balasubramanian, Shayok Chakraborty, Shen Shyang Ho, Harry Wechsler, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceedingChapter

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

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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