Support vector machine algorithm

Doniger S, Hofmann T, Yeh J. Predicting CNS permeability of drug molecules comparison of neural network and support vector machine algorithms. / Comp Biol 2002 9 849-64. 

Support Vector Machine (SVM) is a classification and regression method developed by Vapnik.30 In support vector regression (SVR), the input variables are first mapped into a higher dimensional feature space by the use of a kernel function, and then a linear model is constructed in this feature space. The kernel functions often used in SVM include linear, polynomial, radial basis function (RBF), and sigmoid function. The generalization performance of SVM depends on the selection of several internal parameters of the algorithm (C and e), the type of kernel, and the parameters of the kernel.31... 

Mazzatorta P, Cronin MTD, Benfenati E. A QSAR study of avian oral toxicity using support vector machines and genetic algorithms. QSAR Comb Sci 2006 25 616-28. 

Tang, L.-J., Zhou, Y.-P., Jiang, J.-H., Zou, H.-Y, Wu, H.-L., Shen, G.-L. and Yu, R.-Q. (2007) Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies./. Chem. Inf. Model, 47,1438-1445. 

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