Online Learning With Incremental Feature Space and Bandit Feedback-Reference-Cited by-同舟云学术

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Online Learning With Incremental Feature Space and Bandit Feedback

Published:2023-12-01 Issue:12 Volume:35 Page:12902-12916
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Gu Shilin¹^ORCID,Luo Tingjin¹^ORCID,He Ming²^ORCID,Hou Chenping¹^ORCID

Affiliation:

1. College of Science, National University of Defense Technology, Changsha, Hunan, China

2. Command and Control Engineering College, People's Liberation Army Engineering University, Nanjing, Jiangsu, China

Funder

National Key Research and Development Program of China

Key NSF of China

National Natural Science Foundation of China

NSF for Huxiang Young Talents Program of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10114584.pdf?arnumber=10114584

Reference40 articles.

1. Efficient online bandit multiclass learning with $\tilde{O}(\sqrt{T})$O˜(T) regret;beygelzimer;Proc 34th Int Conf Mach Learn,2017

2. Passive-aggressive bounds in bandit feedback classification;zhong;Proc Eur Conf Mach Learn Princ Pract Knowl Discov Databases,2015

3. Newtron: An efficient bandit algorithm for online multiclass prediction;hazan;Proc Adv Neural Inf Process Syst,2011

4. Online passive-aggressive algorithms;crammer;J Mach Learn Res,2006

5. Boosting with online binary learners for the multiclass bandit problem;chen;Proc Int Conf Mach Learn,2014

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