HMM based Viterbi paths for rejection correction in a convolutional neural network classifier
Identifieur interne : 002705 ( Hal/Corpus ); précédent : 002704; suivant : 002706HMM based Viterbi paths for rejection correction in a convolutional neural network classifier
Auteurs : Hubert Cecotti ; Szilárd Vajda ; Abdel BelaïdSource :
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Abstract
This paper presents a rejection strategy for a convolutional neural network. The method is based on Viterbi paths generated by a context based 2D stochastic model for rejected image correction. The rejection strategy is an important issue in neural network theory. The challenge is to find rules, which determine if an image is correctly classified or not. Applying strong rules leads to the rejection of many well-recognized patterns whereas weak rules do not always involve a strong decrease of erroneous patterns. We propose to use in several ways the knowledge of an external stochastic model so called NSHP-HMM (Non Symmetric Half-Plane Hidden Markov Model) for re-evaluating the rejected patterns.
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The method is based on Viterbi paths generated by a context based 2D stochastic model for rejected image correction. The rejection strategy is an important issue in neural network theory. The challenge is to find rules, which determine if an image is correctly classified or not. Applying strong rules leads to the rejection of many well-recognized patterns whereas weak rules do not always involve a strong decrease of erroneous patterns. We propose to use in several ways the knowledge of an external stochastic model so called NSHP-HMM (Non Symmetric Half-Plane Hidden Markov Model) for re-evaluating the rejected patterns.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">HMM based Viterbi paths for rejection correction in a convolutional neural network classifier</title><author role="aut"><persName><forename type="first">Hubert</forename><surname>Cecotti</surname></persName><email>cecotti@loria.fr</email><idno type="halauthor">59676</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Szilárd</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email><idno type="halauthor">52833</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email></email><idno type="halauthor">52807</idno><affiliation ref="#struct-2362"></affiliation></author><editor role="depositor"><persName><forename>Hubert</forename><surname>Cecotti</surname></persName><email>cecotti@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2005-09-27 17:57:09</date><date type="whenModified">2016-05-19 01:05:41</date><date type="whenReleased">2005-09-27 17:57:09</date><date type="whenProduced">2005-08-29</date></edition><respStmt><resp>contributor</resp><name key="103257"><persName><forename>Hubert</forename><surname>Cecotti</surname></persName><email>cecotti@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00000367</idno><idno type="halUri">https://hal.inria.fr/inria-00000367</idno><idno type="halBibtex">cecotti:inria-00000367</idno><idno type="halRefHtml">IAPR - TC3 International Workshop on Neural Networks and Learning in Document Recognition - NNLDAR 2005, Aug 2005, Seoul, Korea, pp.23-27, 2005</idno><idno type="halRef">IAPR - TC3 International Workshop on Neural Networks and Learning in Document Recognition - NNLDAR 2005, Aug 2005, Seoul, Korea, pp.23-27, 2005</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="1">Not set</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">HMM based Viterbi paths for rejection correction in a convolutional neural network classifier</title><author role="aut"><persName><forename type="first">Hubert</forename><surname>Cecotti</surname></persName><email>cecotti@loria.fr</email><idno type="halAuthorId">59676</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Szilárd</forename><surname>Vajda</surname></persName><email>vajda@loria.fr</email><idno type="halAuthorId">52833</idno><affiliation ref="#struct-2362"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><idno type="halAuthorId">52807</idno><affiliation ref="#struct-2362"></affiliation></author></analytic><monogr><meeting><title>IAPR - TC3 International Workshop on Neural Networks and Learning in Document Recognition - NNLDAR 2005</title><date type="start">2005-08-29</date><settlement>Seoul, Korea</settlement></meeting><imprint><biblScope unit="pp">23-27</biblScope><date type="datePub">2005-08-29</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">character recognition</term><term xml:lang="en">neural network</term><term xml:lang="en">rejection strategy</term><term xml:lang="en">hidden markov model</term><term xml:lang="en">stochastic model</term><term xml:lang="en">viterbi path</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode><classCode scheme="halDomain" n="info.info-ne">Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">This paper presents a rejection strategy for a convolutional neural network. The method is based on Viterbi paths generated by a context based 2D stochastic model for rejected image correction. The rejection strategy is an important issue in neural network theory. The challenge is to find rules, which determine if an image is correctly classified or not. Applying strong rules leads to the rejection of many well-recognized patterns whereas weak rules do not always involve a strong decrease of erroneous patterns. We propose to use in several ways the knowledge of an external stochastic model so called NSHP-HMM (Non Symmetric Half-Plane Hidden Markov Model) for re-evaluating the rejected patterns.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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