TY - GEN
T1 - Online multiclass passive-aggressive learning on a fixed budget
AU - Wu, Chung Hao
AU - His, Wei Chen
AU - Lu, Henry Horng Shing
AU - Hang, Hsueh Ming
PY - 2017/9/25
Y1 - 2017/9/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85032670364&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2017.8050803
DO - 10.1109/ISCAS.2017.8050803
M3 - Conference contribution
AN - SCOPUS:85032670364
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Y2 - 28 May 2017 through 31 May 2017
ER -