Foundations of Computational Mathematics

, Volume 6, Issue 2, pp 145–170 | Cite as

Online Learning Algorithms

Article

Abstract

In this paper, we study an online learning algorithm in Reproducing Kernel Hilbert Spaces (RKHSs) and general Hilbert spaces. We present a general form of the stochastic gradient method to minimize a quadratic potential function by an independent identically distributed (i.i.d.) sample sequence, and show a probabilistic upper bound for its convergence.

Online learning Stochastic approximation Regularization Reproducing Kernel Hilbert Spaces 

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Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Toyota Technological Institute at Chicago, 1427 East 60th Street, Chicago, IL 60637USA
  2. 2.Department of Mathematics, University of California at Berkeley, Berkeley, CA 94720, USA; Current address: Toyota Technological Institute at Chicago, 1427 East 60th Street, Chicago, IL 60637USA

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