On different ensembles of kernel machines

摘要

We study some ensembles of kernel machines. Each machine is first trained on a bootstraped subset of a whole dataset and then, they together are combined linearly by optimizing an objective function. We discuss two different objective functions inspired by boosting methods. The present preliminary experiments show merits and drawbacks of our approach in comparison to standard SVM and bagging SVM.