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 |
|---|---|
| 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 |