@inproceedings{1e4a26b7d2724444bb24ee7d617e35eb,
title = "Online multiclass passive-aggressive learning on a fixed budget",
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.",
author = "Wu, {Chung Hao} and His, {Wei Chen} and Lu, {Henry Horng Shing} and Hang, {Hsueh Ming}",
year = "2017",
month = sep,
day = "25",
doi = "10.1109/ISCAS.2017.8050803",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Symposium on Circuits and Systems",
address = "United States",
note = "50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 ; Conference date: 28-05-2017 Through 31-05-2017",
}