In this paper, we are interested in the analysis of regularized online algorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergence. Explicit learning rates are also given for particular step sizes.
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★The author’s current address: Department of Computer Sciences, University College London, Gower Street, London WC1E, England, UK.
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Ying, Y. Convergence analysis of online algorithms. Adv Comput Math 27, 273–291 (2007). https://doi.org/10.1007/s10444-005-9002-z
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DOI: https://doi.org/10.1007/s10444-005-9002-z
Keywords
- online learning algorithm
- reproducing kernel Hilbert space
- regularized sample error
- general loss function