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AuthorAbdel Hady, Mohamed Faroukdc.contributor.author
Date of accession2016-03-15T06:22:57Zdc.date.accessioned
Available in OPARU since2016-03-15T06:22:57Zdc.date.available
Year of creation2011dc.date.created
AbstractSupervised machine learning is a branch of artificial intelligence concerned with learning computer programs to automatically improve with experience through knowledge extraction from examples. It builds predictive models from labeled data. Such learning approaches are useful for many interesting real-world applications, but are particularly useful for tasks involving the automatic categorization, retrieval and extraction of knowledge from large collections of data such as text, images and videos. In traditional supervised learning, one uses "labeled" data to build a model. However, labeling the training data for real-world applications is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience and training. There are implicit costs associated with obtaining these labels from domain experts, such as limited time and financial resources. This is especially true for applications that involve learning with large number of class labels and sometimes with similarities among them. Semi-supervised learning (SSL) addresses this inherent bottleneck by allowing the model to integrate part or all of the available unlabeled data in its supervised learning. The goal is to maximize the learning performance of the model through such newly-labeled examples while minimizing the work required of human annotators. Exploiting unlabeled data to help improve the learning performance has become a hot topic during the last decade. It is interesting to see that semi-supervised learning and ensemble learning are two important paradigms that were developed almost in parallel and with different philosophies. Semi-supervised learning tries to improve generalization performance by exploiting unlabeled data, while ensemble learning tries to achieve the same objective by using multiple predictors. In this thesis, I concentrate on SSL with committees and especially on co-training style algorithms.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (Fassung vom 01.10.2008)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
KeywordCo-trainingdc.subject
KeywordDempster-Shafer evidence theorydc.subject
KeywordEnsemble learningdc.subject
KeywordLearning from unlabeled datadc.subject
KeywordMultiple classifier systemsdc.subject
KeywordSemi-supervised learningdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHActive learningdc.subject.lcsh
MeSHMan-machine systemsdc.subject.mesh
MeSHNeoplasms; Classificationdc.subject.mesh
TitleSemi-supervised learning with committees: exploiting unlabeled data using ensemble learning algorithmsdc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-1750dc.identifier.doi
PPN648824063dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-75602dc.identifier.urn
GNDData Miningdc.subject.gnd
GNDDempster-Shafer-Theoriedc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2011-02-21T15:27:50Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 13.973; W: W-H 12.439uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS ID7560uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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