Support Vector Machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.
|Series||Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"|
- Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\