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Classification of Paddy by using NAIVE BAYESIAN Classifier

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dc.contributor.author San, Thida
dc.contributor.author Thwe
dc.date.accessioned 2019-07-31T11:47:07Z
dc.date.available 2019-07-31T11:47:07Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1511
dc.description.abstract Classification is a form of data analysis that can be used to extract models describing important data classes or to predict future data trends. Data classification is a two step process. In this system, a model is built on the training datasets by using the Naive Bayesian classification algorithm. And then, a model is used to test the unknown datasets. The performance of classifier is estimated by using the hold-out method. The Naive Bayesian (NB) classifiers have been one of the most popular techniques as basis of many classification applications both theoretically and practically. This system presents a Naive Bayesian classification learning in order to evolve useful subset of paddy features for classification task. This system is determined the kind of paddy by using Naive Bayesian classification. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Classification of Paddy by using NAIVE BAYESIAN Classifier en_US
dc.type Article en_US

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