Tree Kernel Usage in Naive Bayes Classifiers

dc.contributor.authorJungermann, Felix
dc.date.accessioned2012-02-21T15:13:04Z
dc.date.available2012-02-21T15:13:04Z
dc.date.issued2012-02-21
dc.description.abstractWe present a novel approach in machine learning by combining naive Bayes classifiers with tree kernels. Tree kernel methods produce promising results in machine learning tasks containing treestructured attribute values. These kernel methods are used to compare two tree-structured attribute values recursively. Up to now tree kernels are only used in kernel machines like Support Vector Machines or Perceptrons. In this paper, we show that tree kernels can be utilized in a naive Bayes classifier enabling the classifier to handle tree-structured values. We evaluate our approach on three datasets containing tree-structured values. We show that our approach using tree-structures delivers significantly better results in contrast to approaches using non-structured (flat) features extracted from the tree. Additionally, we show that our approach is significantly faster than comparable kernel machines in several settings which makes it more useful in resource-aware settings like mobile devices.en
dc.identifier.urihttp://hdl.handle.net/2003/29316
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-3377
dc.language.isoende
dc.relation.ispartofLWA 2011en
dc.subjectLazy Learningen
dc.subjectNaive Bayes Classifieren
dc.subjectTree Kernelen
dc.subjectTree-structured Valuesen
dc.subject.ddc004
dc.titleTree Kernel Usage in Naive Bayes Classifiersen
dc.typeTextde
dc.type.publicationtypeconferenceObjectde
dcterms.accessRightsopen access

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