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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Frameset//EN""http://www.w3.org/TR/REC-html40/frameset.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc on Wed Sep 04 10:31:48 CDT 2002 --><TITLE>: Package weka.classifiers</TITLE><LINK REL ="stylesheet" TYPE="text/css" HREF="../../stylesheet.css" TITLE="Style"></HEAD><BODY BGCOLOR="white"><!-- ========== START OF NAVBAR ========== --><A NAME="navbar_top"><!-- --></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0"><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3"> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> 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TARGET="_top"><B>NO FRAMES</B></A></FONT></TD></TR></TABLE><!-- =========== END OF NAVBAR =========== --><HR><H2>Package weka.classifiers</H2><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Interface Summary</B></FONT></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="IterativeClassifier.html"><I>IterativeClassifier</I></A></B></TD><TD>Interface for classifiers that can induce models of growing complexity one step at a time.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Sourcable.html"><I>Sourcable</I></A></B></TD><TD>Interface for classifiers that can be converted to Java source.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="UpdateableClassifier.html"><I>UpdateableClassifier</I></A></B></TD><TD>Interface to incremental classification models that can learn using one instance at a time.</TD></TR></TABLE> <P><TABLE BORDER="1" CELLPADDING="3" CELLSPACING="0" WIDTH="100%"><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=2><FONT SIZE="+2"><B>Class Summary</B></FONT></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="AdaBoostM1.html">AdaBoostM1</A></B></TD><TD>Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="AdditiveRegression.html">AdditiveRegression</A></B></TD><TD>Meta classifier that enhances the performance of a regression base classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="AttributeSelectedClassifier.html">AttributeSelectedClassifier</A></B></TD><TD>Class for running an arbitrary classifier on data that has been reduced through attribute selection.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Bagging.html">Bagging</A></B></TD><TD>Class for bagging a classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="BVDecompose.html">BVDecompose</A></B></TD><TD>Class for performing a Bias-Variance decomposition on any classifier using the method specified in:</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="CheckClassifier.html">CheckClassifier</A></B></TD><TD>Class for examining the capabilities and finding problems with classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="ClassificationViaRegression.html">ClassificationViaRegression</A></B></TD><TD>Class for doing classification using regression methods.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Classifier.html">Classifier</A></B></TD><TD>Abstract classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="CostMatrix.html">CostMatrix</A></B></TD><TD>Class for a misclassification cost matrix.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="CostSensitiveClassifier.html">CostSensitiveClassifier</A></B></TD><TD>This metaclassifier makes its base classifier cost-sensitive.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="CVParameterSelection.html">CVParameterSelection</A></B></TD><TD>Class for performing parameter selection by cross-validation for any classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="DecisionStump.html">DecisionStump</A></B></TD><TD>Class for building and using a decision stump.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="DecisionTable.html">DecisionTable</A></B></TD><TD>Class for building and using a simple decision table majority classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="DistributionClassifier.html">DistributionClassifier</A></B></TD><TD>Abstract classification model that produces (for each test instance) an estimate of the membership in each class (ie.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="DistributionMetaClassifier.html">DistributionMetaClassifier</A></B></TD><TD>Class that wraps up a Classifier and presents it as a DistributionClassifier for ease of programmatically handling Classifiers in general -- only the one predict method (distributionForInstance) need be worried about.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Evaluation.html">Evaluation</A></B></TD><TD>Class for evaluating machine learning models.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="EvaluationClient.html">EvaluationClient</A></B></TD><TD>Class for running cross-validation over multiple machines.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="FilteredClassifier.html">FilteredClassifier</A></B></TD><TD>Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="HyperPipes.html">HyperPipes</A></B></TD><TD>Class implementing a HyperPipe classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="IB1.html">IB1</A></B></TD><TD>IB1-type classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="IBk.html">IBk</A></B></TD><TD><i>K</i>-nearest neighbour classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Id3.html">Id3</A></B></TD><TD>Class implementing an Id3 decision tree classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="KernelDensity.html">KernelDensity</A></B></TD><TD>Class for building and using a very simple kernel density classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="LinearRegression.html">LinearRegression</A></B></TD><TD>Class for using linear regression for prediction.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Logistic.html">Logistic</A></B></TD><TD>Class for building and using a two-class logistic regression model with a ridge estimator.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="LogitBoost.html">LogitBoost</A></B></TD><TD>Class for boosting any classifier that can handle weighted instances.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="LWR.html">LWR</A></B></TD><TD>Locally-weighted regression.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="MetaCost.html">MetaCost</A></B></TD><TD>This metaclassifier makes its base classifier cost-sensitive using the method specified in </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="MultiClassClassifier.html">MultiClassClassifier</A></B></TD><TD>Class for handling multi-class datasets with 2-class distribution classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="MultiScheme.html">MultiScheme</A></B></TD><TD>Class for selecting a classifier from among several using cross validation on the training data.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="NaiveBayes.html">NaiveBayes</A></B></TD><TD>Class for a Naive Bayes classifier using estimator classes.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="NaiveBayesSimple.html">NaiveBayesSimple</A></B></TD><TD>Class for building and using a simple Naive Bayes classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="OneR.html">OneR</A></B></TD><TD>Class for building and using a 1R classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Prism.html">Prism</A></B></TD><TD>Class for building and using a PRISM classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="RegressionByDiscretization.html">RegressionByDiscretization</A></B></TD><TD>Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute discretized.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="SMO.html">SMO</A></B></TD><TD>Implements John C.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="Stacking.html">Stacking</A></B></TD><TD>Implements stacking.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="ThresholdSelector.html">ThresholdSelector</A></B></TD><TD>Class for selecting a threshold on a probability output by a distribution classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="UserClassifier.html">UserClassifier</A></B></TD><TD>Class for generating an user defined decision tree.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="VFI.html">VFI</A></B></TD><TD>Class implementing the voting feature interval classifier.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="VotedPerceptron.html">VotedPerceptron</A></B></TD><TD>Implements the voted perceptron algorithm by Freund and Schapire.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="ZeroR.html">ZeroR</A></B></TD><TD>Class for building and using a 0-R classifier.</TD></TR></TABLE> <P><HR><!-- ========== START OF NAVBAR ========== --><A NAME="navbar_bottom"><!-- --></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0"><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_bottom_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3"> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../overview-summary.html"><FONT CLASS="NavBarFont1"><B>Overview</B></FONT></A> </TD> <TD BGCOLOR="#FFFFFF" CLASS="NavBarCell1Rev"> <FONT CLASS="NavBarFont1Rev"><B>Package</B></FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <FONT CLASS="NavBarFont1">Class</FONT> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="package-tree.html"><FONT CLASS="NavBarFont1"><B>Tree</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../deprecated-list.html"><FONT CLASS="NavBarFont1"><B>Deprecated</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../index-all.html"><FONT CLASS="NavBarFont1"><B>Index</B></FONT></A> </TD> <TD BGCOLOR="#EEEEFF" CLASS="NavBarCell1"> <A HREF="../../help-doc.html"><FONT CLASS="NavBarFont1"><B>Help</B></FONT></A> </TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../weka/attributeSelection/package-summary.html"><B>PREV PACKAGE</B></A> <A HREF="../../weka/classifiers/adtree/package-summary.html"><B>NEXT PACKAGE</B></A></FONT></TD><TD 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