Active Learning

Balasubramanian, V N and Chakraborty, S and Ho, S S and Wechsler, S and Panchanathan, S (2014) Active Learning. In: Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Morgan Kaufmann, pp. 49-70. ISBN 978-0-12-398537-8

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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.

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Item Type: Book Section
Additional Information: We thank Lazaro Makili at Instituto Superior Politécnico da Universidade Katyavala Bwila, Benguela, Angola for the kind permission to reproduce images from their article ”Active Learning Using Conformal Predictors: Application to Image Classification” (Fusion Science and Technology, American Nuclear Society, 62:347, 2012) in this chapter. This chapter is based upon work supported by the US National Science Foundation under Grant No. 1116360. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US National Science Foundation.
Uncontrolled Keywords: Active Learning; Face Recognition; Image Classification; Multicriteria Active Learning; Online Active Learning; Query by Transduction
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 19 Nov 2014 11:32
Last Modified: 19 Nov 2014 11:32
URI: http://raiith.iith.ac.in/id/eprint/865
Publisher URL: http://dx.doi.org/10.1016/B978-0-12-398537-8.00003...
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