Abstract
This chapter studies linear SVM algorithms and its application to an online system for the recognition of activities on smartphones (L-HAR). The algorithms differ on the norm of their formulation’s regularization term (whether it is the L1-, L2- or L1-L2-Norm). They allow to control over dimensionality reduction and classification accuracy while increasing the prediction speed when compared with kernelized SVM algorithms. Moreover, this chapter presents a novel approach for training these classifiers (EX-SMO)withminimal effort usingwell-known solvers. To conclude, the benefits of adding smartphones gyroscope into the recognition system are presented along with another feature selection mechanisms that use subsets of features in the time and frequency domain.
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Reyes Ortiz, J.L. (2015). Linear SVM Models for Online Activity Recognition . In: Smartphone-Based Human Activity Recognition. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-14274-6_6
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DOI: https://doi.org/10.1007/978-3-319-14274-6_6
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