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Communication dans un congrès

How to optimize sample in active learning: Dispersion, an optimum criterion for classification ?

Abstract : We want generate learning data appropriated to classification problems. First, we show that theorical results about low discrepancy sequences in regression problems are not adequate for classification problems. Then, we show with theorical and experimental arguments that minimising the dispersion of the sample is a relevant strategy to optimize performance of classification learning.
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Communication dans un congrès
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https://hal.inrae.fr/hal-02591892
Déposant : Migration Irstea Publications <>
Soumis le : vendredi 15 mai 2020 - 15:35:37
Dernière modification le : jeudi 8 octobre 2020 - 17:06:02

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  • HAL Id : hal-02591892, version 1
  • IRSTEA : PUB00026365

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Benoît Gandar, G. Loosli, Guillaume Deffuant. How to optimize sample in active learning: Dispersion, an optimum criterion for classification ?. ENBIS (European Network for Business and Industrial Statistics), Jul 2009, Saint-Etienne, France. ⟨hal-02591892⟩

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