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AuthorThiel, Christiandc.contributor.author
Date of accession2016-03-15T11:04:21Zdc.date.accessioned
Available in OPARU since2016-03-15T11:04:21Zdc.date.available
Year of creation2010dc.date.created
AbstractMultiple classifier systems (MCS) unite the answers of separately-trained powerful base-classifiers to obtain the right classification for the sample at hand. In practical applications, a sample is not associated with exactly one class, but belongs fuzzily to multiple ones. How uncertain class information can be incorporated into multiple classifier systems is detailed in this work. On the theoretical side, it is described how existing approaches to modelling uncertainty like Bayesian probability, Dempster-Shafer theory, fuzzy logic or fuzzy sets, and also unfamiliar ones like the distribution of opinions, are able to support and deal with the core notions of uncertainty in classification: vagueness, imprecision and certainty. In the larger practical part, the use of uncertainty is detailed for every stage of the MCS. The most suitable classifiers are identified, and some well-known schemes extended to deal and answer with uncertain class information. Notably every aspect of the newly proposed and award-winning (KES 2007) fuzzy-input fuzzy-output support vector machines is explained. How the certainty of a classifier answer can be quantified is explored as well as which fusion scheme to use to come to a final classification. The steps for applying the techniques above to real-world problems are shown exemplarily for two applications, the recognition of emotions in facial expression videos, and land cover mapping from satellite images (a winner of the IEEE DFTC Contest 2008). Homepage des Autors: http://www.christianthiel.com/dissertation.htmldc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
ID of original publ.http://www.hut-verlag.de/9783868536751.htmldc.relation.uri
LicenseStandard (ohne Print-On-Demand)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_opod_v1dc.rights.uri
KeywordClassifierdc.subject
KeywordEmotion recognitiondc.subject
KeywordEmotionserkennungdc.subject
KeywordFuzzy-inputdc.subject
KeywordImprecisiondc.subject
KeywordMCSdc.subject
KeywordMehrklassifikatorsystemdc.subject
KeywordMultiple classifiers systemsdc.subject
KeywordUnsicherheitskalküldc.subject
KeywordVaguenessdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHCertaintydc.subject.lcsh
LCSHRemote sensingdc.subject.lcsh
LCSHSupport vector machinesdc.subject.lcsh
LCSHUncertaintydc.subject.lcsh
MeSHMan-machine systemsdc.subject.mesh
TitleMultiple classifier systems incorporating uncertaintydc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3901dc.identifier.doi
PPN644428465dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-75022dc.identifier.urn
GNDDempster-Shafer-Theoriedc.subject.gnd
GNDFuzzy-Integraldc.subject.gnd
GNDFuzzy-Maßdc.subject.gnd
GNDFuzzy-Wahrscheinlichkeitdc.subject.gnd
GNDKlassifikationdc.subject.gnd
GNDKlassifikator <Informatik>dc.subject.gnd
GNDSicherheitdc.subject.gnd
GNDSupport-Vektor-Maschinedc.subject.gnd
GNDUnsicheres Schließendc.subject.gnd
GNDUnsicherheitdc.subject.gnd
GNDWahrscheinlichkeitstheoretische Mengedc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Citation of original publ.Thiel, Christian: Multiple classifier systems incorporating uncertainty. - München: Verl. Dr. Hut, 2010. - ISBN 978-3-86853-675-1uulm.citationOrigPub
Date of activation2011-01-13T14:23:22Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 9.845; W: W-H 12.066uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS-ID7502uulm.vtsID
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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