Journal Title
Title of Journal: Found Comput Math
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Abbravation: Foundations of Computational Mathematics
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Publisher
Springer-Verlag
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Authors: Yiming Ying Massimiliano Pontil
Publish Date: 2007/04/25
Volume: 8, Issue: 5, Pages: 561-596
Abstract
This paper considers the leastsquare online gradient descent algorithm in a reproducing kernel Hilbert space RKHS without an explicit regularization term We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm The essential element in our analysis is the interplay between the generalization error and a weighted cumulative error which we define in the paper We show that although the algorithm does not involve an explicit RKHS regularization term choosing the step sizes appropriately can yield competitive error rates with those in the literature
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