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EnglishIn 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.
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| Titolo: | A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers |
| Autori: | |
| Data di pubblicazione: | 2013 |
| 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 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. |
| Handle: | http://hdl.handle.net/11567/629595 |
| ISBN: | 9783642407277 |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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