Linear SVM Models for Online Activity Recognition

  • Jorge Luis Reyes OrtizEmail author
Part of the Springer Theses book series (Springer Theses)


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.


Classification Accuracy Dimensionality Reduction Time Domain Feature Frequency Domain Feature Prediction Speed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CETpDUniversitat Politècnica de CatalunyaBarcelonaSpain

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