An empirical study on comparison between transfer learning and semi-supervised learning

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dc.contributor.advisorZhang, Huajie
dc.contributor.authorCheng, Ao
dc.date.accessioned2023-03-01T16:29:28Z
dc.date.available2023-03-01T16:29:28Z
dc.date.issued2013
dc.date.updated2016-10-21T00:00:00Z
dc.description.abstractTransfer learning and semi-supervised learning attract considerable attention since the traditional machine learning methods yield insufficient performance in many practical applications with scarce labeled data. In such cases, knowledge transfer from a related domain or information extraction of unlabeled data, if done properly, would significantly upgrade the classifier by avoiding costly labeling expense. These two branches of machine learning try to use auxiliary data to make up for the shortage of labeled instances. In this study, a set of experiments are conducted on several typical algorithms for both transfer learning and semi-supervised learning to test whether these auxiliary data should be beneficial. The empirical study shows that these auxiliary instances may not be permanently helpful comparing to the traditional learning methods. However, when a special situation with an extremely small number of labeled instances arises, the auxiliary data would improve the performance significantly. The internal characteristics influencing the performance in each branch is also explored in this study.
dc.description.copyright© Ao Cheng, 2013
dc.description.noteElectronic Only. (UNB thesis number) Thesis 9246. (OCoLC) 960967924
dc.description.noteM.C.S., University of New Brunswick, Faculty of Computer Science, 2013.
dc.formattext/xml
dc.format.extentx, 79 pages
dc.format.mediumelectronic
dc.identifier.oclc(OCoLC) 960967924
dc.identifier.otherThesis 9246
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13963
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.subject.lcshMachine learning
dc.subject.lcshComputer algorithms
dc.subject.lcshLearning classifier systems
dc.titleAn empirical study on comparison between transfer learning and semi-supervised learning
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.fullnameMaster of Computer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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