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dc.contributor.authorDubey, Rachit.
dc.description.abstractCo-training is a semi supervised learning method that effectively learns from a pool of labeled and unlabeled data and takes advantage of redundancy in the feature set. Co-training is known to work well when the assumptions it makes on the feature set hold true. In this project, we investigated the use of co-training for action recognition for the elderly people. Experimental results showed that co-training was able to boost the performance of action recognition with a very few number of labeled samples. In this project, we also present a new co-training strategy – natural two view learning which doesn’t require the prior existence of two redundant views. Our proposed strategy doesn’t require the feature set to be described with sufficient and redundant view and hence can be applied to a broader class of problems. The Experiments on UCI data sets indicates that the proposed natural two view learning algorithm improves classification accuracy especially when the number of labeled examples is very few.en_US
dc.format.extent47 p.en_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleNatural two view learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWu Jianxin
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.researchCentre for Computational Intelligenceen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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