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Article: Cotransfer learning using coupled markov chains with restart

TitleCotransfer learning using coupled markov chains with restart
Authors
KeywordsIntelligent systems
classification
cotransfer learning
coupled Markov chains
iterative methods
labels ranking
transfer learning
Issue Date2014
Citation
IEEE Intelligent Systems, 2014, v. 29, n. 4, p. 26-33 How to Cite?
Abstract© 2001-2011 IEEE. This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.
Persistent Identifierhttp://hdl.handle.net/10722/277005
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 2.195
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Qingyao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorYe, Yunming-
dc.date.accessioned2019-09-18T08:35:19Z-
dc.date.available2019-09-18T08:35:19Z-
dc.date.issued2014-
dc.identifier.citationIEEE Intelligent Systems, 2014, v. 29, n. 4, p. 26-33-
dc.identifier.issn1541-1672-
dc.identifier.urihttp://hdl.handle.net/10722/277005-
dc.description.abstract© 2001-2011 IEEE. This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.-
dc.languageeng-
dc.relation.ispartofIEEE Intelligent Systems-
dc.subjectIntelligent systems-
dc.subjectclassification-
dc.subjectcotransfer learning-
dc.subjectcoupled Markov chains-
dc.subjectiterative methods-
dc.subjectlabels ranking-
dc.subjecttransfer learning-
dc.titleCotransfer learning using coupled markov chains with restart-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/MIS.2013.32-
dc.identifier.scopuseid_2-s2.0-84907621145-
dc.identifier.volume29-
dc.identifier.issue4-
dc.identifier.spage26-
dc.identifier.epage33-
dc.identifier.isiWOS:000343014700004-
dc.identifier.issnl1541-1672-

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