Linear kernel combination using boosting
Alexis Lechervy, Philippe-Henri Gosselin, Fŕed́eric Precioso · 2012
Abstract. In this paper, we propose a novel algorithm to design multi-class kernels based on an iterative combination of weak kernels in a schema inspired from the boosting framework. Our solution has a complexity lin-ear with the training set size. We evaluate our method for classification on a toy example by integrating our multi-class kernel into a kNN clas-sifier and comparing our results with a reference iterative kernel design method. We also evaluate our method for image categorization by con-sidering a classic image database and comparing our boosted linear kernel combination with the direct linear combination of all features in a linear SVM. 1