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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on signal processing Ročník 70; s. 1947 - 1958
Hlavní autoři:	Huang, Boshuang, Salgia, Sudeep, Zhao, Qing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!