Bayesian classifiers based on kernel density estimation: Flexible classifiers

@article{Martnez2009BayesianCB,
  title={Bayesian classifiers based on kernel density estimation: Flexible classifiers},
  author={Aritz P{\'e}rez Mart{\'i}nez and Pedro Larra{\~n}aga and I{\~n}aki Inza},
  journal={Int. J. Approx. Reasoning},
  year={2009},
  volume={50},
  pages={341-362}
}
When learning Bayesian network based classifiers continuous variables are usually handled by discretization, or assumed that they follow a Gaussian distribution. This work introduces the kernel based Bayesian network paradigm for supervised classification. This paradigm is a Bayesian network which estimates the true density of the continuous variables using kernels. Besides, tree-augmented naive Bayes, k-dependence Bayesian classifier and complete graph classifier are adapted to the novel… CONTINUE READING
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