標題: Budgeted Algorithm for Linearized Confidence-Weighted Learning
作者: Lin, Yu-Shiou
Wu, Chung-Hao
Lu, Henry Horng-Shing
交大名義發表
統計學研究所
電子工程學系及電子研究所
National Chiao Tung University
Institute of Statistics
Department of Electronics Engineering and Institute of Electronics
關鍵字: binary classification;budget;online learning;kernel;resource;confidence
公開日期: 1-一月-2019
摘要: This paper presents a novel algorithm for performing linearized confidence-weighted (LCW) learning on a fixed budget. LCW learning has been applied to solve online classification problems in recent years. To make better classification performance, it is common to combine with kernel functions through the kernel trick. However, the trick makes the LCW learning vulnerable to the curse of kernelization that causes unlimited growth in memory usage and run-time. To address this issue, we first re-interpret the LCW learning by using a resource perspective deeming every instance as a potential resource to exploit. Based on the perspective, we then propose a budgeted algorithm that approximates the LCW learning under a finite constraint on the number of available resources. The proposed algorithm enjoys finite complexities of time and space and thus is able to break the curse. Experiments on several open datasets show that the proposed algorithm approximates the LCW learning well and is competitive to state-of-the-art budgeted algorithms.
URI: http://dx.doi.org/10.1145/3358505.3358510
http://hdl.handle.net/11536/153844
ISBN: 978-1-4503-7165-0
DOI: 10.1145/3358505.3358510
期刊: PROCEEDINGS OF 2019 3RD INTERNATIONAL CONFERENCE ON CLOUD AND BIG DATA COMPUTING (ICCBDC 2019)
起始頁: 6
結束頁: 10
顯示於類別:會議論文