Large datasets: a mixed method to adapt and improve their learning by neural networks used in regression contexts
Identifieur interne : 002E31 ( Hal/Curation ); précédent : 002E30; suivant : 002E32Large datasets: a mixed method to adapt and improve their learning by neural networks used in regression contexts
Auteurs : Marc Sauget [France] ; Julien Henriet [France] ; Michel Salomon [France] ; Sylvain Contassot-Vivier [France]Source :
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Abstract
The purpose of this work is to further study the relevance of accelerating the Monte-Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit~\cite{Vecpar08b}. Our parallel algorithm consists in an optimized decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, although that decomposition provides subsets of similar signal complexities, their sizes may be quite different, still implying potential differences in their learning times. This paper presents an efficient data extraction allowing a good and balanced training without any loss of signal information. As will be shown, the resulting irregular decomposition permits an important improvement in the learning time of the global network.
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DOI: 10.1007/978-3-642-23957-1_21
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type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1007/978-3-642-23957-1_21</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Data extraction</term><term>Doses Distributions</term><term>External radiotherapy</term><term>Learning algorithms</term><term>Neural Networks</term><term>Pre-clinical studies</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The purpose of this work is to further study the relevance of accelerating the Monte-Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit~\cite{Vecpar08b}. Our parallel algorithm consists in an optimized decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, although that decomposition provides subsets of similar signal complexities, their sizes may be quite different, still implying potential differences in their learning times. This paper presents an efficient data extraction allowing a good and balanced training without any loss of signal information. As will be shown, the resulting irregular decomposition permits an important improvement in the learning time of the global network.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Large datasets: a mixed method to adapt and improve their learning by neural networks used in regression contexts</title><author role="aut"><persName><forename type="first">Marc</forename><surname>Sauget</surname></persName><email></email><idno type="halauthor">661610</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394832"></affiliation></author><author role="aut"><persName><forename type="first">Julien</forename><surname>Henriet</surname></persName><email></email><idno type="halauthor">661611</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394832"></affiliation></author><author role="aut"><persName><forename type="first">Michel</forename><surname>Salomon</surname></persName><email></email><idno type="halauthor">661612</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394833"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Contassot-Vivier</surname></persName><email></email><idno type="halauthor">661613</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2346"></affiliation></author><editor role="depositor"><persName><forename>Sylvain</forename><surname>Contassot-Vivier</surname></persName><email>sylvain.contassotvivier@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2011-11-23 09:42:57</date><date type="whenModified">2016-05-18 08:55:31</date><date type="whenReleased">2011-11-23 09:42:57</date><date type="whenProduced">2011-09-15</date></edition><respStmt><resp>contributor</resp><name key="122046"><persName><forename>Sylvain</forename><surname>Contassot-Vivier</surname></persName><email>sylvain.contassotvivier@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00643870</idno><idno type="halUri">https://hal.inria.fr/hal-00643870</idno><idno type="halBibtex">sauget:hal-00643870</idno><idno type="halRefHtml">Lazaros Iliadis and Chrisina Jayne. 12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks - EANN 2011 and 7th IFIP WG 12.5 International Conference - AIAI 2011, Sep 2011, Corfu, Greece. Springer, 363, pp.182--191, 2011, IFIP Advances in Information and Communication Technology. <10.1007/978-3-642-23957-1_21></idno><idno type="halRef">Lazaros Iliadis and Chrisina Jayne. 12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks - EANN 2011 and 7th IFIP WG 12.5 International Conference - AIAI 2011, Sep 2011, Corfu, Greece. Springer, 363, pp.182--191, 2011, IFIP Advances in Information and Communication Technology. <10.1007/978-3-642-23957-1_21></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-FCOMTE">Université de Franche-Comté</idno><idno type="stamp" n="UNIV-BM">Université de Technologie de Belfort-Montbeliard</idno><idno type="stamp" n="ENSMM">Ecole Nationale Supérieure de Mécanique et des Microtechniques</idno><idno type="stamp" n="FEMTO-ST" p="UNIV-FCOMTE">Franche-Comté Electronique, Mécanique, Thermique et Optique - Sciences et Technologies</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-NSS" p="LORIA">Réseaux, systèmes et services</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="UNIV-BM-THESE">Université de Technologie de Belfort-Montbeliard</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Large datasets: a mixed method to adapt and improve their learning by neural networks used in regression contexts</title><author role="aut"><persName><forename type="first">Marc</forename><surname>Sauget</surname></persName><idno type="halAuthorId">661610</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394832"></affiliation></author><author role="aut"><persName><forename type="first">Julien</forename><surname>Henriet</surname></persName><idno type="halAuthorId">661611</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394832"></affiliation></author><author role="aut"><persName><forename type="first">Michel</forename><surname>Salomon</surname></persName><idno type="halAuthorId">661612</idno><orgName ref="#struct-242365"></orgName><affiliation ref="#struct-394833"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Contassot-Vivier</surname></persName><idno type="halAuthorId">661613</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2346"></affiliation></author></analytic><monogr><meeting><title>12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks - EANN 2011 and 7th IFIP WG 12.5 International Conference - AIAI 2011</title><date type="start">2011-09-15</date><date type="end">2011-09-18</date><settlement>Corfu</settlement><country key="GR">Greece</country></meeting><editor>Lazaros Iliadis and Chrisina Jayne</editor><imprint><publisher>Springer</publisher><biblScope unit="serie">IFIP Advances in Information and Communication Technology</biblScope><biblScope unit="volume">363</biblScope><biblScope unit="pp">182--191</biblScope><date type="datePub">2011-09-15</date></imprint></monogr><idno type="doi">10.1007/978-3-642-23957-1_21</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Pre-clinical studies</term><term xml:lang="en">Doses Distributions</term><term xml:lang="en">Neural Networks</term><term xml:lang="en">Learning algorithms</term><term xml:lang="en">External radiotherapy</term><term xml:lang="en">Data extraction</term></keywords><classCode scheme="halDomain" n="info.info-dc">Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">The purpose of this work is to further study the relevance of accelerating the Monte-Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit~\cite{Vecpar08b}. Our parallel algorithm consists in an optimized decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, although that decomposition provides subsets of similar signal complexities, their sizes may be quite different, still implying potential differences in their learning times. This paper presents an efficient data extraction allowing a good and balanced training without any loss of signal information. As will be shown, the resulting irregular decomposition permits an important improvement in the learning time of the global network.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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