Fast bounded online gradient descent algorithms for scalable kernel-based online learning

• Main Authors: ZHAO, Peilin, WANG, Jialei, WU, Pengcheng, JIN, Rong, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Computationally efficient; Empirical performance; Gradient descent; Gradient descent algorithms; Large-scale datasets; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2342; https://ink.library.smu.edu.sg/context/sis_research/article/3342/viewcontent/Fast_Bounded_Online_Gradient_Descent_Algorithms_for_Scalable_Kernel_Based_Online_Learning.pdf
• Institution: Singapore Management University

Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, w...