Refined bounds for online pairwise learning algorithms

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

14 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)2656-2665
Journal / PublicationNeurocomputing
Volume275
Online published2 Dec 2017
Publication statusPublished - 31 Jan 2018

Abstract

Motivated by the recent growing interest in pairwise learning problems, we study the generalization performance of Online Pairwise lEaRning Algorithm (OPERA) in a reproducing kernel Hilbert space (RKHS) without an explicit regularization. The convergence rates established in this paper can be arbitrarily closed to O (T 1/2 )  within T iterations and largely improve the existing convergence rates for OPERA. Our novel analysis is conducted by showing an almost boundedness of the iterates encountered in the learning process with high probability after establishing an induction lemma on refining the RKHS norm estimate of the iterates.

Research Area(s)

  • Learning theory, Online learning, Pairwise learning, Reproducing Kernel Hilbert Space

Citation Format(s)

Refined bounds for online pairwise learning algorithms. / Chen, Xiaming; Lei, Yunwen.
In: Neurocomputing, Vol. 275, 31.01.2018, p. 2656-2665.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review