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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
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
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
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