Reduction of Large Training Set by Guided Progressive Sampling: Application to Neonatal Intensive Care Data - Archive ouverte HAL
Communication Dans Un Congrès Année : 2007

Reduction of Large Training Set by Guided Progressive Sampling: Application to Neonatal Intensive Care Data

Résumé

Although large training sets are supposed to improve the performance of learning algorithms, there are limits to the volume of data such an algorithm can handle. To overcome this problem, we describe an improvement to a progressive sampling method by guiding the construction of a reduced training set. The application of this method to neonatal intensive care data shows that it is possible to reduce a training set to a third of its original size without decreasing performance.

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Intelligence artificielle [cs.AI]
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François Portet  : Connectez-vous pour contacter le contributeur

https://hal.science/hal-01006112

Soumis le : vendredi 13 juin 2014-17:13:47

Dernière modification le : mardi 27 août 2024-12:45:07

Archivage à long terme le : samedi 13 septembre 2014-11:35:10

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Dates et versions

hal-01006112 , version 1 (13-06-2014)

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  • HAL Id : hal-01006112 , version 1

Citer

François Portet, Feng Gao, Jim Hunter, René Quiniou. Reduction of Large Training Set by Guided Progressive Sampling: Application to Neonatal Intensive Care Data. in Intelligent Data International Workshop on Analysis in Medicine and Pharmacology (IDAMAP2007), Jul 2007, Amsterdam, Netherlands. pp.1-2. ⟨hal-01006112⟩

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