A primal sub-gradient method for structured classification with the averaged sum loss

• Autorzy: Dejan Mančev , Branimir Todorović
• Języki publikacji: EN

Abstrakty

EN

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. We show that the proposed version with the averaged sum loss has at least the same guarantees in terms of the prediction loss as the stochastic version. Experiments are conducted on two sequence labeling problems, shallow parsing and part-of-speech tagging, and also include a comparison with other popular sequential structured learning algorithms.

Słowa kluczowe

EN

structured classification; support vector machines; sub-gradient methods; sequence labeling

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 4
• Strony: 917-930

Daty

wydano

2014

otrzymano

2013-11-05

poprawiono

2014-04-14

poprawiono

2014-05-26

Twórcy

autor

Dejan Mančev

• Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, Niš, Serbia

autor

Branimir Todorović

• Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, Niš, Serbia

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Identyfikatory

DOI

10.2478/amcs-2014-0067