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dc.contributor.advisor De Silva, C
dc.contributor.author Wimalawarne, KADNK
dc.date.accessioned 2011-03-29T09:10:28Z
dc.date.available 2011-03-29T09:10:28Z
dc.identifier.citation Wimalawarne, K.A.D.N.K. (2008). Face recognition using kernel classifiers [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.mrt.ac.lk/handle/123/614
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/614
dc.description CD-Rom included ; A Dissertation submitted to the Department of Computer Science and Engineering for the MSc in Computer Science en_US
dc.description.abstract Face reorganization to be one of the biggest challengers to the machine learning community Over three decades of extensive research has been earned out in this field by many researchers. In spite of many face recognition methods developed. Research on novel methods are needed m fulfill need of modern application// In the recent past kernel methods have teen successful!) applied to FACE recognition. We present a novel approach in face recognition with informative vector a machine a sparse gauss ion tor process kernel classifier. Experiments with the ORL . face database shows that recognition accuracies both these algorithms to be comparable . but informative vector machine . we also found that using automatic relevance determination kernel which with informative vector machine pan ides .1 novel approach lo dimension reduction is feature space. Overall. both sparse solution and dimension reduction* with informative vector machine reduces the storage space and computational cost while achieving a recognition accuracy close to supports vector machines
dc.format.extent viii, 68p. : ill., photos. en_US
dc.language.iso en en_US
dc.subject COMPUTER SCIENCE - Dissertation
dc.subject COMPUTER SCIENCE AND ENGINEERING - Dissertation
dc.subject MACHINE LEARNING - Face Recognition
dc.title Face recognition using kernel classifiers
dc.type Thesis-Abstract
dc.identifier.faculty Engineering en_US
dc.identifier.degree MSc en_US
dc.identifier.department Department of Computer Science & Engineering en_US
dc.date.accept 2008-01
dc.identifier.accno 92283 en_US


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