Can AdaBoost.M1 Learn Incrementally? A Comparison to Learn + + Under Different Combination Rules
Identifieur interne : 001103 ( Main/Exploration ); précédent : 001102; suivant : 001104Can AdaBoost.M1 Learn Incrementally? A Comparison to Learn + + Under Different Combination Rules
Auteurs : Syed Mohammed [États-Unis] ; James Leander [États-Unis] ; Matthew Marbach [États-Unis] ; Robi Polikar [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: We had previously introduced Learn + + , inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn + + and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost’s distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.
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DOI: 10.1007/11840817_27
Affiliations:
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A Comparison to Learn + + Under Different Combination Rules</title><author><name sortKey="Mohammed, Syed" sort="Mohammed, Syed" uniqKey="Mohammed S" first="Syed" last="Mohammed">Syed Mohammed</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ</wicri:regionArea><placeName><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Leander, James" sort="Leander, James" uniqKey="Leander J" first="James" last="Leander">James Leander</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ</wicri:regionArea><placeName><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Marbach, Matthew" sort="Marbach, Matthew" uniqKey="Marbach M" first="Matthew" last="Marbach">Matthew Marbach</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ</wicri:regionArea><placeName><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Polikar, Robi" sort="Polikar, Robi" uniqKey="Polikar R" first="Robi" last="Polikar">Robi Polikar</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ</wicri:regionArea><placeName><region type="state">New Jersey</region></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">États-Unis</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2006</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">89B2C13D8DC226C233E7C1AFBB2E03C4F59DA7E3</idno><idno type="DOI">10.1007/11840817_27</idno><idno type="ChapterID">27</idno><idno type="ChapterID">Chap27</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We had previously introduced Learn + + , inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn + + and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost’s distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>New Jersey</li></region></list><tree><country name="États-Unis"><region name="New Jersey"><name sortKey="Mohammed, Syed" sort="Mohammed, Syed" uniqKey="Mohammed S" first="Syed" last="Mohammed">Syed Mohammed</name></region><name sortKey="Leander, James" sort="Leander, James" uniqKey="Leander J" first="James" last="Leander">James Leander</name><name sortKey="Marbach, Matthew" sort="Marbach, Matthew" uniqKey="Marbach M" first="Matthew" last="Marbach">Matthew Marbach</name><name sortKey="Polikar, Robi" sort="Polikar, Robi" uniqKey="Polikar R" first="Robi" last="Polikar">Robi Polikar</name><name sortKey="Polikar, Robi" sort="Polikar, Robi" uniqKey="Polikar R" first="Robi" last="Polikar">Robi Polikar</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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