Supported vector machine

N. Christiani, J. Shawe-Taylor, Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000. 

Jorissen RN, Gilson MK. Virtual screening of molecular databases using a Support Vector Machine. J Chem Inf Model 2005 45 549-61. 

Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Pletnev IV. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 2003 43(6) 2048-56. 

Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge, UK Cambridge University Press, 2000. 

PM-CSVM positive majority consensus support vector machines... 

Ivanciuc, O. Applications of support vector machines in chemistry. In Reviews in Computational Chemistry, Upkowitz,... 

A variety of other QSAR-type models for the prediction of plasma protein binding have also been published recently, including neural networks/support vector machines [64], 4-D fingerprints [65], and TOPS-MODE descriptors [66]. 

Ovidiu Ivanciuc, Applications of Support Vector Machines in Chemistry. 

Tobita, M., Nishikawa, T. and Nagashima, R. (2005) A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorganic el Medicinal Chemistry Letters, 15, 2886-2890. 

Christianini, N., Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, NY, 2000. Crawford, L. R., Morrison, J. D. Anal. Chem. 40, 1968, 1469-1474. Computer methods in analytical mass spectrometry. Empirical identification of molecular class. 

Meyer, D., Leisch, F., Homik, K. Neurocomputing 55, 2003, 169-186. The support vector machine under test. 

Steinwart, I., Christmann, A. Support Vector Machines. Springer, New York, 2008. 

Thissen, U., Pepers, M., Ustiin, B., Meissen, W. J., Buydens, L. C. M. Chemom. Intell. Lab. Syst. 73, 2004, 169-179. Comparing support vector machines to PLS for spectral regression applications. 

Xu, Y., Zomer, S., Brereton, R. G. Crit. Rev. Anal. Chem. 34, 2006, 177-188. Support vector machines A recent method for classification in chemometrics. 

Self-organizing map Singular value decomposition Support vector machine... 

Huanxiang L, Xiaojun Y, Ruisheng Zh, Mancang L, Zhide H, Botao F (2005) Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. J. Phys. Chem. Sect B. 109 20565-20571. 

Support Vector Machines (SVMs) generate either linear or nonlinear classifiers depending on the so-called kernel [149]. The kernel is a matrix that performs a transformation of the data into an arbitrarily high-dimensional feature-space, where linear classification relates to nonlinear classifiers in the original space the input data lives in. SVMs are quite a recent Machine Learning method that received a lot of attention because of their superiority on a number of hard problems [150]. 

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