Practical aspects of SVM regression

Support vector machines were initially developed for class discrimination, and most of their applications have been for pattern classification. SVM classification is especially relevant for important cheminformatics problems, such as recognizing drug-like compounds, or discriminating between toxic and nontoxic compounds, and many such applications have been published. The QSAR applications of SVM regression, however, are rare, and this is unfortunate because it represents a viable alternative to multiple linear regression, PLS, or neural networks. In this section, we present several SVMR applications to QSAR datasets, and we compare the performance of several kernels. 

0and i = 1,2,3). All SVM models were computed with mySVM, by Riiping, (http //www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/). 

SVM Regression OSAR for the Phenol Toxicity to Tetrahymena pyriformis 

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