It is known that support vector machines (SVMs), based on statistical learning theory, are an efficient approach to solving the pattern recognition problem because of their remarkable performance in terms of prediction accuracy. When applying SVMs, the input vectors should be normalized. The prediction performance would differ according to the normalization method used. Thus, it is important to choose an efficient method for normalizing input vectors as this could improve the prediction performance of the SVMs. In this paper, various normalization methods for input vectors have been studied and the best normalization method proposed to achieve the best performance in automatic incident detection. The experimental results show that the performance of an automatic incident detection system using SVMs can be highly dependent on the method used in normalizing the input vectors, and that the proposed normalization method is the most efficient method for automatic incident detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Input Vector Normalization Methods in Support Vector Machines for Automatic Incident Detection


    Beteiligte:
    Kim, Daehyon (Autor:in) / Lee, Seungjae (Autor:in) / Cho, Seongkil (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2007-12-01


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    Incident detection using support vector machines

    Yuan, Fang / Cheu, Ruey Long | Elsevier | 2003


    Incident detection using support vector machines

    Yuan, Fang | Online Contents | 2003


    Support Vector Machine Models in Freeway Incident Detection

    Cheu, R. L. / Srinivasan, D. / Teh, E. T. et al. | British Library Conference Proceedings | 2003


    Lane detection using support vector machines

    Kim, Singhoon / Park, Jeongho / Cho, Seong-Ik et al. | Tema Archiv | 2007