Abstract
In this paper, we present a new compactly supported kernel for SVM based image recognition. This kernel which we called Geometric Compactly Supported (GCS) can be viewed as a generalization of spherical kernels to higher dimensions. The construction of the GCS kernel is based on a geometric approach using the intersection volume of two n-dimensional balls. The compactness property of the GCS kernel leads to a sparse Gram matrix which enhances computation efficiency by using sparse linear algebra algorithms. Comparisons of the GCS kernel performance, for image recognition task, with other known kernels prove the interest of this new kernel.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Boughorbel, S., Tarel, JP., Fleuret, F., Boujemaa, N. (2005). The GCS Kernel for SVM-Based Image Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_94
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DOI: https://doi.org/10.1007/11550907_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
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