Abstract
In this paper we analyze Support Vector Machine (SVM) algorithm to the problem of chemical compounds screening with a desired activity, definition of hits. The support vector machine transforms the input data in an (unknown) high dimensional feature space and the kernel technique is applied to calculate the inner-product of feature data.The problem of automatically tuning multiple parameters for pattern recognition SVMs using our new introduced kernel for chemical compounds is considered. This is done by simple eigen analysis method which is applied to the matrix of the same dimension as the kernel matrix to find the structure of feature data, and to find the kernel parameter accordingly. We characterize distribution of data by the principle component analysis method.
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Kozak, K., Stapor, K. (2008). Classes of Kernels for Hit Definition in Compound Screening. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_59
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DOI: https://doi.org/10.1007/978-3-540-69731-2_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
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