Table of contents

  1. Front Matter
  2. K. Pelckmans, I. Goethals, J.D. Brabanter, J.A.K. Suykens, B.D. Moor
    Pages 77-98
  3. P. Mitra, C.A. Murthy, S.K. Pal
    Pages 99-111
  4. K. Huang, H. Yang, I. King, M.R. Lyu
    Pages 113-131
  5. M. Vogt, V. Kecman
    Pages 133-158
  6. D. Anguita, A. Boni, S. Ridella, F. Rivieccio, D. Sterpi
    Pages 159-179
  7. Q. Song, W. Hu, X. Yang
    Pages 219-232
  8. J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos
    Pages 275-296
  9. J. Brezmes, E. Llobet, S. Al-Khalifa, S. Maldonado, J.W. Gardner
    Pages 365-386
  10. J.L. Rojo-Álvarez, A. García-Alberola, A. Artés-Rodríguez, Á Arenal-Maíz
    Pages 413-431

About this book


The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in the respective fields.


Data Mining Fuzzy Kernel Machines Pattern Recognition Soft Computing Statistical Learning algorithm algorithms bioinformatics cognition learning learning theory machine learning model proving

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin/Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-24388-5
  • Online ISBN 978-3-540-32384-6
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site