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 which depend on general conditions on the iterative step sizes.
Original language | English |
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Pages (from-to) | 19-36 |
Number of pages | 18 |
Journal | International Journal of Pure and Applied Mathematics |
Volume | 46 |
Issue number | 1 |
State | Published - 1 Jan 2008 |