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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc (build 1.5.0_13) on Mon Jul 14 01:37:02 CEST 2008 --><TITLE>Uses of Class com.rapidminer.operator.learner.meta.AbstractMetaLearner (RapidMiner Class Documentation)</TITLE><LINK REL ="stylesheet" TYPE="text/css" HREF="../../../../../../stylesheet.css" TITLE="Style"><SCRIPT type="text/javascript">function windowTitle(){    parent.document.title="Uses of Class com.rapidminer.operator.learner.meta.AbstractMetaLearner (RapidMiner Class Documentation)";}</SCRIPT><NOSCRIPT></NOSCRIPT></HEAD><BODY BGCOLOR="white" onload="windowTitle();"><!-- ========= START OF TOP NAVBAR ======= --><A NAME="navbar_top"><!-- --></A><A HREF="#skip-navbar_top" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- 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performance.&nbsp;</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.tree"><B>com.rapidminer.operator.learner.tree</B></A></TD><TD>Provides decision tree learners.&nbsp;</TD></TR></TABLE>&nbsp;<P><A NAME="com.rapidminer.operator.learner.meta"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Uses of <A HREF="../../../../../../com/rapidminer/operator/learner/meta/AbstractMetaLearner.html" title="class in com.rapidminer.operator.learner.meta">AbstractMetaLearner</A> in <A HREF="../../../../../../com/rapidminer/operator/learner/meta/package-summary.html">com.rapidminer.operator.learner.meta</A></FONT></TH></TR></TABLE>&nbsp;<P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Subclasses of <A HREF="../../../../../../com/rapidminer/operator/learner/meta/AbstractMetaLearner.html" title="class in com.rapidminer.operator.learner.meta">AbstractMetaLearner</A> in <A HREF="../../../../../../com/rapidminer/operator/learner/meta/package-summary.html">com.rapidminer.operator.learner.meta</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/AbstractStacking.html" title="class in com.rapidminer.operator.learner.meta">AbstractStacking</A></B></CODE><BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;This class uses n+1 inner learners and generates n different models by using the last n learners.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/AdaBoost.html" title="class in com.rapidminer.operator.learner.meta">AdaBoost</A></B></CODE><BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;This AdaBoost implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/AdditiveRegression.html" title="class in com.rapidminer.operator.learner.meta">AdditiveRegression</A></B></CODE><BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;This operator uses  regression learner as a base learner.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/Bagging.html" title="class in com.rapidminer.operator.learner.meta">Bagging</A></B></CODE><BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;This Bagging implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/BayBoostStream.html" title="class in com.rapidminer.operator.learner.meta">BayBoostStream</A></B></CODE><BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Assumptions:  target label is always boolean goal is to fit a crisp ensemble classifier (use_distribution always off) base classifier weights are always adapted by a single row from first to last no internal bootstrapping </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE>&nbsp;class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../../com/rapidminer/operator/learner/meta/BayesianBoosting.html" title="class in com.rapidminer.operator.learner.meta">BayesianBoosting</A></B></CODE>

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