Support Vector Machine to Synthesise Kernels

  • Hongying Meng
  • John Shawe-Taylor
  • Sandor Szedmak
  • Jason D. R. Farquhar
Conference paper

DOI: 10.1007/11559887_15

Volume 3635 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Meng H., Shawe-Taylor J., Szedmak S., Farquhar J.D.R. (2005) Support Vector Machine to Synthesise Kernels. In: Winkler J., Niranjan M., Lawrence N. (eds) Deterministic and Statistical Methods in Machine Learning. Lecture Notes in Computer Science, vol 3635. Springer, Berlin, Heidelberg

Abstract

In this paper, we introduce a new method (SVM_2K) which amalgamates the capabilities of the Support Vector Machine (SVM) and Kernel Canonical Correlation Analysis (KCCA) to give a more sophisticated combination rule that the boosting framework allows. We show how this combination can be achieved within a unified optimisation model to create a consistent learning rule which combines the classification abilities of the individual SVMs with the synthesis abilities of KCCA. To solve the unified problem, we present an algorithm based on the Augmented Lagrangian Method. Experiments show that SVM_2K performs well on generic object recognition problems in computer vision.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hongying Meng
    • 1
  • John Shawe-Taylor
    • 1
  • Sandor Szedmak
    • 1
  • Jason D. R. Farquhar
    • 1
  1. 1.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK