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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.bayes.NaiveBayes
weka.classifiers.bayes.NaiveBayesUpdateable
public class NaiveBayesUpdateable
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.
@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
-O Display model in old format (good when there are many classes)
| Constructor Summary | |
|---|---|
NaiveBayesUpdateable()
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| Method Summary | |
|---|---|
java.lang.String |
getRevision()
Returns the revision string. |
TechnicalInformation |
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on. |
java.lang.String |
globalInfo()
Returns a string describing this classifier |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
void |
setUseSupervisedDiscretization(boolean newblah)
Set whether supervised discretization is to be used. |
| Methods inherited from class weka.classifiers.bayes.NaiveBayes |
|---|
buildClassifier, displayModelInOldFormatTipText, distributionForInstance, getCapabilities, getDisplayModelInOldFormat, getOptions, getUseKernelEstimator, getUseSupervisedDiscretization, listOptions, setDisplayModelInOldFormat, setOptions, setUseKernelEstimator, toString, updateClassifier, useKernelEstimatorTipText, useSupervisedDiscretizationTipText |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Methods inherited from interface weka.classifiers.UpdateableClassifier |
|---|
updateClassifier |
| Constructor Detail |
|---|
public NaiveBayesUpdateable()
| Method Detail |
|---|
public java.lang.String globalInfo()
globalInfo in class NaiveBayespublic TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlergetTechnicalInformation in class NaiveBayespublic void setUseSupervisedDiscretization(boolean newblah)
setUseSupervisedDiscretization in class NaiveBayesnewblah - true if supervised discretization is to be used.public java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class NaiveBayespublic static void main(java.lang.String[] argv)
argv - the options
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