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Online Learning Algorithms

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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.

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Authors and Affiliations

  1. Toyota Technological Institute at Chicago, 1427 East 60th Street, Chicago, IL 60637, USA

    Steve Smale

  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 60637, USA

    Yuan Yao

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  1. Steve Smale
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  2. Yuan Yao
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Correspondence to Steve Smale or Yuan Yao.

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Smale, S., Yao, Y. Online Learning Algorithms. Found Comput Math 6, 145–170 (2006). https://doi.org/10.1007/s10208-004-0160-z

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  • DOI: https://doi.org/10.1007/s10208-004-0160-z

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