Chapter

Deterministic and Statistical Methods in Machine Learning

Volume 3635 of the series Lecture Notes in Computer Science pp 242-255

Support Vector Machine to Synthesise Kernels

  • Hongying MengAffiliated withSchool of Electronics and Computer Science, University of Southampton
  • , John Shawe-TaylorAffiliated withSchool of Electronics and Computer Science, University of Southampton
  • , Sandor SzedmakAffiliated withSchool of Electronics and Computer Science, University of Southampton
  • , Jason D. R. FarquharAffiliated withSchool of Electronics and Computer Science, University of Southampton

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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.