Additive versus exponentiated gradient updates for linear prediction
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TL;DR
The main methodological idea is using a distance function between weight vectors both in motivating the algorithms and as a potential function in an amortized analysis that leads to worst-case loss bounds.
Abstract
Article Additive versus exponentiated gradient updates for linear prediction Share on Authors: Jyrki Kivinen Department of Computer Science, P.O. Box 26 (Teollisuuskatu 23) FIN-00014 University of Helsinki, Finland Department of Computer Science, P.O. Box 26 (Teollisuuskatu 23) FIN-00014 University of Helsinki, FinlandView Profile , Manfred K. Warmuth Computer and Information Sciences, University of California, Santa Cruz, Santa Cruz, CA Computer and Information Sciences, University of California, Santa Cruz, Santa Cruz, CAView Profile Authors Info & Claims STOC '95: Proceedings of the twenty-seventh annual ACM symposium on Theory of computingMay 1995 Pages 209–218https://doi.org/10.1145/225058.225121Online:29 May 1995Publication History 48citation1,649DownloadsMetricsTotal Citations48Total Downloads1,649Last 12 Months41Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access