A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Kernels for Chemical Compounds in Biological Screening
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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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.