Active Learning

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

doi:10.1016/B978-0-12-398537-8.00003-1

Active Learning

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English (US)
Title of host publication	Conformal Prediction for Reliable Machine Learning
Subtitle of host publication	Theory, Adaptations and Applications
Publisher	Elsevier Inc.
Pages	49-70
Number of pages	22
ISBN (Print)	9780123985378
DOIs	https://doi.org/10.1016/B978-0-12-398537-8.00003-1
State	Published - Apr 2014

Keywords

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

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1016/B978-0-12-398537-8.00003-1

Cite this

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title = "Active Learning",

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.",

keywords = "Active Learning, Face Recognition, Image Classification, Multicriteria Active Learning, Online Active Learning, Query by Transduction",

author = "Balasubramanian, \{Vineeth N.\} and Shayok Chakraborty and Ho, \{Shen Shyang\} and Harry Wechsler and Sethuraman Panchanathan",

note = "Funding Information: We thank Lazaro Makili at Instituto Superior Polit{\'e}cnico da Universidade Katyavala Bwila, Benguela, Angola for the kind permission to reproduce images from their article {\textquotedblright}Active Learning Using Conformal Predictors: Application to Image Classification{\textquotedblright} (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. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",

year = "2014",

month = apr,

doi = "10.1016/B978-0-12-398537-8.00003-1",

language = "English (US)",

isbn = "9780123985378",

pages = "49--70",

booktitle = "Conformal Prediction for Reliable Machine Learning",

publisher = "Elsevier Inc.",

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AU - Balasubramanian, Vineeth N.

AU - Chakraborty, Shayok

AU - Ho, Shen Shyang

AU - Wechsler, Harry

AU - Panchanathan, Sethuraman

N1 - Funding 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. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2014/4

Y1 - 2014/4

N2 - 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.

AB - 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.

KW - Active Learning

KW - Face Recognition

KW - Image Classification

KW - Multicriteria Active Learning

KW - Online Active Learning

KW - Query by Transduction

UR - https://www.scopus.com/pages/publications/84902228612

UR - https://www.scopus.com/pages/publications/84902228612#tab=citedBy

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M3 - Chapter

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SN - 9780123985378

SP - 49

EP - 70

BT - Conformal Prediction for Reliable Machine Learning

PB - Elsevier Inc.

ER -

Active Learning

Abstract

Keywords

ASJC Scopus subject areas

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