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Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.
This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.
For more information on Naive Bayes classifiers, see
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
BibTeX:
@inproceedings{John1995,
address = {San Mateo},
author = {George H. John and Pat Langley},
booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence},
pages = {338-345},
publisher = {Morgan Kaufmann},
title = {Estimating Continuous Distributions in Bayesian Classifiers},
year = {1995}
}
Valid options are:
-K
Use kernel density estimator rather than normal
distribution for numeric attributes
-D
Use supervised discretization to process numeric attributes
author:
Len Trigg (trigg@cs.waikato.ac.nz)
author:
Eibe Frank (eibe@cs.waikato.ac.nz)
version:
$Revision: 1.9 $ |
