# A Cross Entropy Algorithm for Classification with $\delta$−Patterns

Abstract : A classification strategy based on $\delta$-patterns is developed via a combinatorial optimization problem related with the maximal clique generation problem on a graph. The proposed solution uses the cross entropy method and has the advantage to be particularly suitable for large datasets. This study is tailored for the particularities of the genomic data.
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Communication dans un congrès
Chassaing, Philippe and others. Fourth Colloquium on Mathematics and Computer Science Algorithms, Trees, Combinatorics and Probabilities, 2006, Nancy, France. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AG, Fourth Colloquium on Mathematics and Computer Science Algorithms, Trees, Combinatorics and Probabilities, pp.399-402, 2006, DMTCS Proceedings
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Littérature citée [7 références]

https://hal.inria.fr/hal-01184688
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Dernière modification le : lundi 16 juillet 2018 - 15:10:58
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dmAG0132.pdf
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• HAL Id : hal-01184688, version 1

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Gabriela Alexe, Gyan Bhanot, Adriana Climescu-Haulica. A Cross Entropy Algorithm for Classification with $\delta$−Patterns. Chassaing, Philippe and others. Fourth Colloquium on Mathematics and Computer Science Algorithms, Trees, Combinatorics and Probabilities, 2006, Nancy, France. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AG, Fourth Colloquium on Mathematics and Computer Science Algorithms, Trees, Combinatorics and Probabilities, pp.399-402, 2006, DMTCS Proceedings. 〈hal-01184688〉

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