A primal sub-gradient method for structured classification with the averaged sum loss - Mančev, Dejan; Todorović, Branimir (Zielona Góra: Uniwersytet Zielonogórski)
We present a primal sub-gradient method for structured SVM optimization defined with the averaged sum of hinge losses inside each example. Compared with the mini-batch version of the Pegasos algorithm for the structured case, which deals with a single structure from each of multiple examples, our algorithm considers multiple structures from a single example in one update. This approach should increase the amount of information learned from the example.
Keywords: structured classification; support vector machines; sub-gradient methods; sequence labeling