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

Author
Anguita, D.; Ghio, A.; Oneto, L.; Reyes, J.; Ridella, S.
Type of activity
Presentation of work at congresses
Name of edition
23rd International Conference on Artificial Neural Networks
Date of publication
2013
Book of congress proceedings
Artificial Neural Networks and Machine Learning – ICANN 2013 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013 Proceedings
First page
434
Last page
441
DOI
https://doi.org/10.1007/978-3-642-40728-4_55 Open in new window
Repository
http://hdl.handle.net/2117/20950 Open in new window
URL
http://link.springer.com/chapter/10.1007%2F978-3-642-40728-4_55 Open in new window
Abstract
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...
Citation
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.
Keywords
Human Activity Recognition, L1-L2 Regularization, Sequential Minimal Optimization algorithm, Support Vector Machine

Participants

  • Anguita, Davide  (author and speaker )
  • Ghio, Alessandro  (author and speaker )
  • Oneto, Luca  (author and speaker )
  • Reyes Ortiz, Jorge Luis  (author and speaker )
  • Ridella, Sandro  (author and speaker )