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http://www.ivanciuc.org/Files/Reprint/Ivanciuc_SVM_CCR_2007_23_291.pdf
292 Applications of Support Vector Machines in Chemistry these two-dimensional objects that belong to two classes (class þ1 and class 1), it is easy to find a line that separates them perfectly. For any particular set of two-class objects, an SVM finds the unique
https://www.researchgate.net/publication/222536579_Support_Vector_Machines_and_Its_Applications_in_Chemistry
We selected support vector machine classifier method (epsilon-SVM) which was most commonly used in QSPR and QSAR studies to optimize the value of kernel parameter g (gamma) [53]. ...
https://www.worldscientific.com/worldscibooks/10.1142/5589
Aug 01, 2004 · In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance.
https://www.springer.com/gp/book/9783319022994
Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence.
http://www.eurekaselect.com/openurl/content.php?genre=article&issn=1568-0266&volume=6&issue=15&spage=1593
Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties.
http://support-vector-machines.org/
Support vector machines (SVM) are a group of supervised learning methods that can be applied to classification or regression. Support vector machines represent an extension to nonlinear models of the generalized portrait algorithm developed by Vladimir Vapnik. The SVM algorithm is based on the statistical learning theory and the Vapnik-Chervonenkis (VC) dimension introduced by Vladimir Vapnik and Alexey Chervonenkis. Review: Applications …
https://www.springer.com/gp/book/9780387772417
Since their appearance in the early nineties, support vector machines and related kernel-based methods have been successfully applied in diverse fields of application such as bioinformatics, fraud detection, construction of insurance tariffs, direct marketing, and data and text mining.
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