Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Naturalistic use of cell phones in driving and context-based user assistance
Proceedings of the 9th international conference on Human computer interaction with mobile devices and services
Analysis and comparative evaluation of discrete tangent estimators
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
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The aim of this paper is to present a novelty methodology to develop similarity measures for classification of time series. First, a linear segmentation algorithm to obtain a section-wise representation of the series is presented. Then, two similarity measures are defined from the differences between the behavior of the series and the level of the series, respectively. The method is applied to subjective-data on time series generated through the evaluations of the driving risk from a group of traffic safety experts. These series are classified using the proposed similarities as kernels for the training of a Support Vector Machine. The results are compared with other classifiers using our similarities, their linear combination and the raw data. The proposed methodology has been successfully evaluated on several databases.