Online multiclass passive-aggressive learning on a fixed budget

Chung Hao Wu, Wei Chen His, Henry Horng Shing Lu*, Hsueh Ming Hang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents a budgetary learning algorithm for online multiclass classification. Based on the multiclass passive-aggressive learning with kernels, we introduce a dual perspective that gives rise to the proposed budgetary algorithm. Basically, the proposed algorithm limits the amount of data in use and fully exploits the available data on hand through optimization. The algorithm has both constant time and space complexities and thus can avoid the curse of kernelization. Experimental results with open datasets show that the proposed budgetary algorithm is competitive with state-of-the-art algorithms.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467368520
DOIs
StatePublished - 25 Sep 2017
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 28 May 201731 May 2017

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
CountryUnited States
CityBaltimore
Period28/05/1731/05/17

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