Combining Discriminant Models with new Multi-Class SVMs

Yann Guermeur 1
1 MODBIO - Computational models in molecular biology
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The idea of performing model combination, instead of model selection, has a long theoretical background in statistics. However, making use of theoretical results is ordinarily subject to the satisfaction of strong hypotheses (weak error correlation, availability of large training sets, possibility to rerun the training procedure an arbitrary number of times, etc.). In contrast, the practitioner is frequently faced with the problem of combining a given set of pretrained classifiers, with highly correlated errors, using only a small training sample. Overfitting is then the main risk, which cannot be overcome but with a strict complexity control of the combiner selected. This suggests that SVMs should be well suited for these difficult situations. Investigating this idea, we introduce a family of multi-class SVMs and assess them as ensemble methods on a real-world problem. This task, protein secondary structure prediction, is an open problem in biocomputing for which model combination appears to be an issue of central importance. Experimental evidence highlights the gain in quality resulting from combining some of the most widely used prediction methods with our SVMs rather than with the ensemble methods traditionally used in the field. The gain increases when the outputs of the combiners are post-processed with a DP algorithm.
Type de document :
Article dans une revue
Pattern Analysis and Applications, Springer Verlag, 2002, 5 (2), pp.168-179
Liste complète des métadonnées

https://hal.inria.fr/inria-00100721
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:49:59
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

Identifiants

  • HAL Id : inria-00100721, version 1

Collections

Citation

Yann Guermeur. Combining Discriminant Models with new Multi-Class SVMs. Pattern Analysis and Applications, Springer Verlag, 2002, 5 (2), pp.168-179. 〈inria-00100721〉

Partager

Métriques

Consultations de la notice

90