Classification of time series is an important task in various fields, e.g., medicine, finance, and industrial. applications. This work discusses strong temporal classification using machine learning techniques. Here, two problems must be solved: the detection of those time instances when the class labels change and the correct assignment of the labels. For this purpose the scenario-based random forest algorithm and a segment and label approach are introduced. The latter is realized with either the augmented dynamic time warping similarity measure or with interpretable generalized radial basis function classifiers. The main application presented in this work is the detection and categorization of car crashes using machine learning. Depending on the crash severity different safety systems, e.g., belt tensioners or airbags must be deployed at time instances when the best-possible protection of passengers is assured.


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    Title :

    Machine learning techniques for time series classification


    Additional title:

    Lernmaschinen-Techniken für die Zeitreihenklassifizierung


    Contributors:

    Published in:

    Publication date :

    2009


    Size :

    209 Seiten, Bilder, Tabellen, Quellen



    Type of media :

    Theses


    Type of material :

    Print


    Language :

    English





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