Beyond Boosting: Recursive ECOC Learning Machines
Identifieur interne : 004814 ( Istex/Corpus ); précédent : 004813; suivant : 004815Beyond Boosting: Recursive ECOC Learning Machines
Auteurs : Elizabeth Tapia ; José C. González ; Alexander Hütermann ; Javier GarcíaSource :
- [ 0302-9743 ]
Abstract
Abstract: We present a wide experimental work evaluating the behaviour of Recursive ECOC (RECOC) [1] learning machines based on Low Density Parity Check (LDPC) coding structures. We show that owing to the iterative decoding algorithms behind LDPC codes, RECOC multiclass learning is progressively achieved. This learning behaviour confirms the existence of new boosting dimension, the one provided by the coding space. We present a method for searching potential good RECOC codes from LDPC ones. Starting from a properly selected LDPC code, we assess the effect of boosting in both weak and strong binary learners. For nearly all domains, we find that boosting a strong learner like a Decision Tree is as effective as boosting a weak one like a Decision Stump. This surprising result substantiates the hypothesis that weakening strong classifiers by boosting has a decorrelation effect, which can be used to improve RECOC learning.
Url:
DOI: 10.1007/978-3-540-25966-4_6
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: We present a wide experimental work evaluating the behaviour of Recursive ECOC (RECOC) [1] learning machines based on Low Density Parity Check (LDPC) coding structures. We show that owing to the iterative decoding algorithms behind LDPC codes, RECOC multiclass learning is progressively achieved. This learning behaviour confirms the existence of new boosting dimension, the one provided by the coding space. We present a method for searching potential good RECOC codes from LDPC ones. Starting from a properly selected LDPC code, we assess the effect of boosting in both weak and strong binary learners. For nearly all domains, we find that boosting a strong learner like a Decision Tree is as effective as boosting a weak one like a Decision Stump. This surprising result substantiates the hypothesis that weakening strong classifiers by boosting has a decorrelation effect, which can be used to improve RECOC learning.</div>
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<abstract>Abstract: We present a wide experimental work evaluating the behaviour of Recursive ECOC (RECOC) [1] learning machines based on Low Density Parity Check (LDPC) coding structures. We show that owing to the iterative decoding algorithms behind LDPC codes, RECOC multiclass learning is progressively achieved. This learning behaviour confirms the existence of new boosting dimension, the one provided by the coding space. We present a method for searching potential good RECOC codes from LDPC ones. Starting from a properly selected LDPC code, we assess the effect of boosting in both weak and strong binary learners. For nearly all domains, we find that boosting a strong learner like a Decision Tree is as effective as boosting a weak one like a Decision Stump. This surprising result substantiates the hypothesis that weakening strong classifiers by boosting has a decorrelation effect, which can be used to improve RECOC learning.</abstract>
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<ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Elizabeth</GivenName>
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<Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>José</GivenName>
<GivenName>C.</GivenName>
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<Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Alexander</GivenName>
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<Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Javier</GivenName>
<FamilyName>García</FamilyName>
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<Contact><Email>javiergv@sip.ucm.es</Email>
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<Affiliation ID="Aff18"><OrgDivision>Department of Electronic Engineering</OrgDivision>
<OrgName>National University of Rosario</OrgName>
<OrgAddress><Country>Argentina</Country>
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<Affiliation ID="Aff19"><OrgDivision>Department of Telematic Engineering</OrgDivision>
<OrgName>Technical University of Madrid</OrgName>
<OrgAddress><Country>Spain</Country>
</OrgAddress>
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<Affiliation ID="Aff20"><OrgDivision>Department of Informatic Systems</OrgDivision>
<OrgName>Complutense University of Madrid</OrgName>
<OrgAddress><Country>Spain</Country>
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<Para>We present a wide experimental work evaluating the behaviour of Recursive ECOC (RECOC) [1] learning machines based on Low Density Parity Check (LDPC) coding structures. We show that owing to the iterative decoding algorithms behind LDPC codes, RECOC multiclass learning is progressively achieved. This learning behaviour confirms the existence of new boosting dimension, the one provided by the coding space. We present a method for searching potential good RECOC codes from LDPC ones. Starting from a properly selected LDPC code, we assess the effect of boosting in both weak and strong binary learners. For nearly all domains, we find that boosting a strong learner like a Decision Tree is as effective as boosting a weak one like a Decision Stump. This surprising result substantiates the hypothesis that weakening strong classifiers by boosting has a decorrelation effect, which can be used to improve RECOC learning.</Para>
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<name type="personal"><namePart type="given">Alexander</namePart>
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<abstract lang="en">Abstract: We present a wide experimental work evaluating the behaviour of Recursive ECOC (RECOC) [1] learning machines based on Low Density Parity Check (LDPC) coding structures. We show that owing to the iterative decoding algorithms behind LDPC codes, RECOC multiclass learning is progressively achieved. This learning behaviour confirms the existence of new boosting dimension, the one provided by the coding space. We present a method for searching potential good RECOC codes from LDPC ones. Starting from a properly selected LDPC code, we assess the effect of boosting in both weak and strong binary learners. For nearly all domains, we find that boosting a strong learner like a Decision Tree is as effective as boosting a weak one like a Decision Stump. This surprising result substantiates the hypothesis that weakening strong classifiers by boosting has a decorrelation effect, which can be used to improve RECOC learning.</abstract>
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<subTitle>5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004. Proceedings</subTitle>
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<name type="personal"><namePart type="given">Fabio</namePart>
<namePart type="family">Roli</namePart>
<affiliation>Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, 09123, Cagliari, Italy</affiliation>
<affiliation>E-mail: roli@diee.unica.it</affiliation>
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<name type="personal"><namePart type="given">Josef</namePart>
<namePart type="family">Kittler</namePart>
<affiliation>Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK</affiliation>
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<namePart type="family">Kanade</namePart>
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<name type="personal"><namePart type="given">Jon</namePart>
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<affiliation>Cornell University, Ithaca, NY, USA</affiliation>
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<name type="personal"><namePart type="given">Friedemann</namePart>
<namePart type="family">Mattern</namePart>
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<name type="personal"><namePart type="given">John</namePart>
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<name type="personal"><namePart type="given">Bernhard</namePart>
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<name type="personal"><namePart type="given">Moshe</namePart>
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