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.
|Title of host publication
|IEEE International Symposium on Circuits and Systems
|Subtitle of host publication
|From Dreams to Innovation, ISCAS 2017 - Conference Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Published - 25 Sep 2017
|50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 28 May 2017 → 31 May 2017
|Proceedings - IEEE International Symposium on Circuits and Systems
|50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
|28/05/17 → 31/05/17