- Tytuł:
- A primal sub-gradient method for structured classification with the averaged sum loss
- Autorzy:
-
Mančev, D.
Todorović, B. - Powiązania:
- https://bibliotekanauki.pl/articles/331050.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
structured classification
support vector machine (SVM)
subgradient method
sequence labeling
klasyfikacja strukturalna
maszyna wektorów nośnych
rozpoznawanie wzorca - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 917-930
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł