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oai:recercat.cat:2117/209502026-02-09T08:04:06Zcom_2072_1033col_2072_452950

A novel procedure for training L1-L2 support vector machine classifiers Anguita, Davide Ghio, Alessandro Oneto, Luca Reyes Ortiz, Jorge Luis Ridella, Sandro Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Àrees temàtiques de la UPC::Informàtica::Automàtica i control Computational algorithms Human Activity Recognition L1-L2 Regularization Sequential Minimal Optimization algorithm Support Vector Machine Algorismes computacionals 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 and L2 SVMs, as well as to fine tune the trade-off between dimensionality reduction and classification accuracy. This scope is of clear importance in applications on resource-limited devices, such as smartphones, like the one we consider to verify the main advantages of the proposed approach: the UCI Human Activity Recognition real-world dataset. Peer Reviewed Postprint (published version) 2013 Conference report 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. 9783642407277 https://hdl.handle.net/2117/20950 10.1007/978-3-642-40728-4_55 eng http://link.springer.com/chapter/10.1007%2F978-3-642-40728-4_55 Restricted access - publisher's policy 8 p. application/pdf