Support vector machines SVMs

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]. 

Support vector machines (SVM) Intestinal absorption Quantitative No [18]... 

Histone deacetylases (HDACs) play a critical role in transcription regulation. Small molecule HDAC inhibitors have become an emerging target for the treatment of cancer and other cell proliferation diseases. We have employed variable selection k nearest neighbor approach (iNN)and support vector machines (SVM) approach to generate QSAR models for 59 chemically diverse... 

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... 

Frenandez Pierna, J. A., Baeten, V., Renier, A. Michotte, Cogdill, R. P. and Dardenne, P. (2005) Combination of support vector machines (SVM) and near infrared (NIR) imaging spectroscopy for the detection of meat and bonemeat (MBM) in compound feeds. J. Chemomet. 18, 341-9. 

Support vector machines In addition to more traditional classification methods like clustering or partitioning, other computational approaches have recently also become popular in chemoinformatics and support vector machines (SVMs) (Warmuth el al. 2003) are discussed here as an example. Typically, SVMs are applied as classifiers for binary property predictions, for example, to distinguish active from inactive compounds. Initially, a set of descriptors is selected and training set molecules are represented as vectors based on their calculated descriptor values. Then linear combinations of training set vectors are calculated to construct a hyperplane in descriptor space that best separates active and inactive compounds, as illustrated in Figure 1.9. 

The most recent advance in machine-learning modeling to gamer widespread application by fields outside of artificial intelligence itself is the support vector machine (SVM). SVM s were first developed by Vapnik in 1992. ... 

Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, et al. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 2006 217 105-19. 

---