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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 | |
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NaiveBayesUpdateable()
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Method Summary | |
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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 |
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buildClassifier, displayModelInOldFormatTipText, distributionForInstance, getCapabilities, getDisplayModelInOldFormat, getOptions, getUseKernelEstimator, getUseSupervisedDiscretization, listOptions, setDisplayModelInOldFormat, setOptions, setUseKernelEstimator, toString, updateClassifier, useKernelEstimatorTipText, useSupervisedDiscretizationTipText |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Methods inherited from interface weka.classifiers.UpdateableClassifier |
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updateClassifier |
Constructor Detail |
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public NaiveBayesUpdateable()
Method Detail |
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public java.lang.String globalInfo()
globalInfo
in class NaiveBayes
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
getTechnicalInformation
in class NaiveBayes
public void setUseSupervisedDiscretization(boolean newblah)
setUseSupervisedDiscretization
in class NaiveBayes
newblah
- true if supervised discretization is to be used.public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class NaiveBayes
public static void main(java.lang.String[] argv)
argv
- the options
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