General algorithmic frameworks for online problems
Yair Censor (yairmath.haifa.ac.il)
Abstract: We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms that depend on general conditions on the iterative step sizes.
Keywords: Online learning, general algorithms, classification, regression, multiclass, convex feasibility.
Category 1: Applications -- Science and Engineering (Basic Sciences Applications )
Category 2: Convex and Nonsmooth Optimization (Convex Optimization )
Citation: International Journal of Pure and Applied Mathematics, Vol. 46 (2008), pp. 19-36.
Entry Submitted: 04/22/2008
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|