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A novel procedure for training L1-L2 support vector machine classifiers

Autor
Anguita, D.; Ghio, A.; Oneto, L.; Reyes, J.; Ridella, S.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
23rd International Conference on Artificial Neural Networks
Any de l'edició
2013
Llibre d'actes
Artificial Neural Networks and Machine Learning – ICANN 2013 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013 Proceedings
Pàgina inicial
434
Pàgina final
441
DOI
https://doi.org/10.1007/978-3-642-40728-4_55 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/20950 Obrir en finestra nova
URL
http://link.springer.com/chapter/10.1007%2F978-3-642-40728-4_55 Obrir en finestra nova
Resum
In this work we propose a novel algorithm for training L1-L2 Support Vector Machine (SVM) classifiers. L1-L2 SVMs allow to combine the effectiveness of L2 models and the feature selection characteristics of L1 solutions. The proposed training approach for L1-L2 SVM requires a minimal effort for its implementation, relying on the exploitation of well-known and widespread tools already developed for conventional L2 SVMs. Moreover, the proposed method is flexible, as it allows to train L1, L1-L2 an...
Citació
Anguita, D. [et al.]. A novel procedure for training L1-L2 support vector machine classifiers. A: International Conference on Artificial Neural Networks. "23rd International Conference on Artificial Neural Networks, ICANN 2013". Sofia: 2013, p. 434-441.
Paraules clau
Human Activity Recognition, L1-L2 Regularization, Sequential Minimal Optimization algorithm, Support Vector Machine

Participants

  • Anguita, Davide  (autor ponent)
  • Ghio, Alessandro  (autor ponent)
  • Oneto, Luca  (autor ponent)
  • Reyes Ortiz, Jorge Luis  (autor ponent)
  • Ridella, Sandro  (autor ponent)