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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorRenooij, Silja
dc.contributor.authorWojcik, Thomas
dc.date.accessioned2023-05-25T02:01:02Z
dc.date.available2023-05-25T02:01:02Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/43929
dc.description.abstractNaïve Bayesian Networks (NB) have been proven to be decently accurate classifiers, even in cases where their independency assumption does not hold. An approach to relax the independency assumption is to search through the possible single dependencies that can be added to the network, creating a so called Tree Augmented Bayesian Network (TAN), with the intention to improve the performance of the network. However, these TAN classifiers often perform about as good as a NB classifier, while increas
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectNaïve Bayesian Networks (NB) have been proven to be decently accurate classifiers, even in cases where their independency assumption does not hold. An approach to relax the independency assumption is to search through the possible single dependencies that can be added to the network, creating a so called Tree Augmented Bayesian Network (TAN), with the intention to improve the performance of the network. However, these TAN classifiers often perform about as good as a NB classifier, while increas
dc.titleWhat explains the difference between naive Bayesian classifiers and tree-augmented Bayesian network classifiers.
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsBayesian Network, classification, Tree Augmented Bayesian Network, NB, TAN
dc.subject.courseuuComputing Science
dc.thesis.id16915


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