Sparse Online Learning via Truncated Gradient : Appendix 1 Proof of Main Results

  • Published 2009
In the setting of standard online learning, we are interested in sequential prediction problems where for i = 1, 2, . . .: 1. An unlabeled example xi = [xi , . . . , x d i ] ∈ R arrives. 2. We make a prediction ŷi based on the current weights wi = [w i , . . . , w d i ] ∈ R. 3. We observe yi, let zi = (xi, yi), and incur some known loss L(wi, zi) convex in… (More)