SVM (Support Vector Machine) is a supervised learning algorithm which is well adapted for high dimensional problems. We implement the John Platt's SMO (sequential minimal optimization) algorithm into Tanagra.
In this tutorial, we show how to implement SVM with TANAGRA. We compare the classifier performance with that of the lineardiscriminant analysis (LDA). The error rate is measured using the bootstrap resamppling approach.
Keywords: SVM, support vector machine, machine à vaste marge, analyse discriminante linéaire, fonction noyau
Components: Supervised Learning, SVM, Linear discriminant analysis, Bootstrap
Tutorial: en_Tanagra_SVM.pdf
Dataset: sonar.xls
References: Wikipedia – « Support vector machine »
