• DocumentCode
    2788512
  • Title

    Multi-class SVM optimization using MCE training with application to topic identification

  • Author

    Hazen, Timothy J.

  • Author_Institution
    MIT Lincoln Lab., Lexington, MA, USA
  • fYear
    2010
  • fDate
    14-19 March 2010
  • Firstpage
    5350
  • Lastpage
    5353
  • Abstract
    This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.
  • Keywords
    pattern classification; support vector machines; ASR lattice output; Fisher corpus; MCE training; human-human telephone conversations; minimum classification error; multiclass SVM optimization; support vector machine; topic identification; Automatic speech recognition; Calibration; Kernel; Laboratories; Lattices; Natural languages; Support vector machine classification; Support vector machines; Telephony; Virtual manufacturing; MCE training; SVM classifiers; topic identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-4295-9
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2010.5494948
  • Filename
    5494948
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2788512