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Conference Papers Year : 2012

Linear kernel combination using boosting


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.
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hal-00753155 , version 1 (17-11-2012)


  • HAL Id : hal-00753155 , version 1


Alexis Lechervy, Philippe-Henri Gosselin, Frédéric Precioso. Linear kernel combination using boosting. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.6. ⟨hal-00753155⟩
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