標題: Online Multiclass Passive-Aggressive Learning on a Fixed Budget
作者: Wu, Chung-Hao
Hsi, Wei-Chen
Lu, Henry Horng-Shing
Hang, Hsueh-Ming
統計學研究所
電子工程學系及電子研究所
Institute of Statistics
Department of Electronics Engineering and Institute of Electronics
公開日期: 1-Jan-2017
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.
URI: http://hdl.handle.net/11536/150700
ISSN: 0271-4302
期刊: 2017 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
起始頁: 2058
結束頁: 2061
Appears in Collections:Conferences Paper