Support Vector Machine to Synthesise Kernels
Hongying Meng, John Shawe-Taylor, Sandor Szedmak and Jason Farquhar
In: Deterministic and Statistical Methods in Machine Learning Lecture Notes in Computer Science , Lecture Notes in Artificial Intelligence . (2005) Springer-Verlag , Berlin, Germany , pp. 242-255. ISBN 3-540-29073-7

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.

[Edit]