Active Learning for Object Classification : From Exploration to Exploitation

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CEBRON, Nicolas, Michael R. BERTHOLD, 2008. Active Learning for Object Classification : From Exploration to Exploitation. In: Data Mining and Knowledge Discovery. 18(2), pp. 283-299. ISSN 1384-5810. eISSN 1573-756X

@article{Cebron2008Activ-3028, title={Active Learning for Object Classification : From Exploration to Exploitation}, year={2008}, doi={10.1007/s10618-008-0115-0}, number={2}, volume={18}, issn={1384-5810}, journal={Data Mining and Knowledge Discovery}, pages={283--299}, author={Cebron, Nicolas and Berthold, Michael R.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/3028"> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:50Z</dc:date> <dcterms:abstract xml:lang="eng">Classifying large datasets without any a-priori information poses a problem in numerous tasks. Especially in industrial environments, we often encounter diverse measurement devices and sensors that produce huge amounts of data, but we still rely on a human expert to help give the data a meaningful interpretation. As the amount of data that must be manually classified plays a critical role, we need to reduce the number of learning episodes involving human interactions as much as possible. In addition for real world applications it is fundamental to converge in a stable manner to a solution that is close to the optimal solution. We present a new self-controlled exploration/exploitation strategy to select data points to be labeled by a domain expert where the potential of each data point is computed based on a combination of its representativeness and the uncertainty of the classifier. A new Prototype Based Active Learning (PBAC) algorithm for classification is introduced. We compare the results to other active learning approaches on several benchmark datasets.</dcterms:abstract> <dc:rights>deposit-license</dc:rights> <dcterms:title>Active Learning for Object Classification : From Exploration to Exploitation</dcterms:title> <dcterms:bibliographicCitation>Data Mining and Knowledge Discovery ; 18 (2009), 2. - S. 283-299</dcterms:bibliographicCitation> <dcterms:issued>2008</dcterms:issued> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103416863-3868037-7"/> <dc:language>eng</dc:language> <dc:contributor>Cebron, Nicolas</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/3028"/> <dc:creator>Berthold, Michael R.</dc:creator> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:50Z</dcterms:available> <dc:creator>Cebron, Nicolas</dc:creator> </rdf:Description> </rdf:RDF>

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