Disagreement-Based Active Learning in Online Settings

We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner either queries the label of the current instance or predicts the label based on past seen examples. The objective is to minimize the number of queries wh...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 70; pp. 1947 - 1958
Main Authors:	Huang, Boshuang, Salgia, Sudeep, Zhao, Qing
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active learning Algorithms Classification Complexity Complexity theory Distance learning Errors label complexity Learning theory Lower bounds Machine learning online learning Optimization Particle separators Picture archiving and communication systems Prediction algorithms Queries Real-time systems regret Signal processing algorithms statistical learning theory Tradeoffs
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!