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Dispersion effect on generalisation error in classification: Experimental proof and practical algorithm

Abstract : Recent theoretical work proposes criteria of dispersion to generate learning points. The aim of this paper is to convince the reader, with experimental proofs, that dispersion is a good criterion in practice for generating learning points for classification problems. Problem of generating learning points consists then in generating points with the lowest dispersion. As a consequence, we present low dispersion algorithms existing in the literature, analyze them and propose a new algorithm.
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Conference papers
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https://hal.inrae.fr/hal-02596424
Contributor : Migration Irstea Publications <>
Submitted on : Friday, May 15, 2020 - 9:00:31 PM
Last modification on : Tuesday, March 23, 2021 - 5:22:03 PM

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  • HAL Id : hal-02596424, version 1
  • IRSTEA : PUB00034342

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Benoît Gandar, G. Loosli, Guillaume Deffuant. Dispersion effect on generalisation error in classification: Experimental proof and practical algorithm. ICAART 2011 Conference .3rd Congference on Agents and Artificial Intelligence, Jan 2011, Rome, Italy. pp.4. ⟨hal-02596424⟩

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