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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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. DSMML 2004. Lecture Notes in Computer Science(), vol 3635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559887_15
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DOI: https://doi.org/10.1007/11559887_15
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