Skip to Main Content
Classifier combination has been an important research area because of their contribution to the accuracy and robustness. Supervised linear combiner types are shown to be strong combiners; but nonlinear types are not well investigated. In this work, we show a method to obtain non-linear versions of simple linear combiner types. Experiments are conducted on four different databases and results are examined. It is observed that we can obtain better accuracies with non-linear combinations for certain types.