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

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題IEEE International Symposium on Circuits and Systems
主出版物子標題From Dreams to Innovation, ISCAS 2017 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781467368520
DOIs
出版狀態Published - 25 九月 2017
事件50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
持續時間: 28 五月 201731 五月 2017

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
ISSN(列印)0271-4310

Conference

Conference50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
國家United States
城市Baltimore
期間28/05/1731/05/17

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