A novel procedure for training L1-L2 support vector machine classifiers
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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.
CitacióAnguita, 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.
Versió de l'editorhttp://link.springer.com/chapter/10.1007%2F978-3-642-40728-4_55
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