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Linear kernel combination using boosting

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.
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Contributor : Philippe-Henri Gosselin <>
Submitted on : Saturday, November 17, 2012 - 6:47:19 PM
Last modification on : Monday, January 25, 2021 - 3:16:03 PM
Long-term archiving on: : Monday, February 18, 2013 - 3:42:37 AM


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  • 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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