General algorithmic frameworks for online problems

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.


International Journal of Pure and Applied Mathematics, Vol. 46 (2008), pp. 19-36.