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A scalable learning algorithm for Kernel Probabilistic Classifier

Serrurier, Mathieu and Prade, Henri A scalable learning algorithm for Kernel Probabilistic Classifier. (2013) In: 7th International Conference on Scalable Uncertainty Management (SUM), 16 September 2013 - 18 September 2013 (Washington, DC, United States).

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Official URL: http://dx.doi.org/10.1007/978-3-642-40381-1_23

Abstract

In this paper we propose a probabilistic classification algorithm that learns a set of kernel functions that associate a probability distribution over classes to an input vector. This model is obtained by maximizing a measure over the probability distributions through a local optimization process. This measure focuses on the faithfulness of the whole probability distribution induced rather than only considering the probabilities of the classes separately. We show that, thanks to a pre-processing computation, the complexity of the evaluation of this measure with respect to a model is no longer dependent on the size of the training set. This makes the local optimization of the whole set of kernel functions tractable, even for large databases. We experiment our method on five benchmark datasets and the KDD Cup 2012 dataset.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in Lecture Notes in Computer Science Volume 8078 ISSN 0302-9743 ISBN 978-3-642-40380-4 The original PDF is available at: http://link.springer.com/chapter/10.1007%2F978-3-642-40381-1_23
HAL Id:hal-01171944
Audience (conference):National conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:15 Jun 2015 14:20

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