Abstract In this work, a support vector machine (SVM) model was developed to predict the hot deformation flow curves of AZ91 magnesium alloy. The experimental stress-strain curves, obtained from hot compression testing at different deformation conditions, were sampled. Consequently, a data base with the input variables of the deformation temperature, strain rate and strain and the output variab...

Support vector machine modeling has become increasingly popular in chemoinformatics. Recently, several advanced support vector machine applications have been reported including, among others, multitask learning for ligand-target prediction. Here, we introduce another support vector machine approach to add compound potency information to similarity searching and enrich database selection sets wi...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

This technical report provides an overview on the support vector machines that have been designed during the recent years for the identification of microRNA sequences. Our aim is to arrive in the end to identify potential ways in which the quality of such machine learning methods could be further improved, knowing that the rate of false positves they produce is still too high.