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<BR> This is the abstract superclass for the support vector machine / KLR implementations of Stefan Rüping.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/GPLearner.html" title="class in com.rapidminer.operator.learner.functions.kernel">GPLearner</A></B></CODE><BR> Gaussian Process (GP) Learner.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/JMySVMLearner.html" title="class in com.rapidminer.operator.learner.functions.kernel">JMySVMLearner</A></B></CODE><BR> This learner uses the Java implementation of the support vector machine <em>mySVM</em> by Stefan Rüping.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/KernelLogisticRegression.html" title="class in com.rapidminer.operator.learner.functions.kernel">KernelLogisticRegression</A></B></CODE><BR> This operator determines a logistic regression model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/LibSVMLearner.html" title="class in com.rapidminer.operator.learner.functions.kernel">LibSVMLearner</A></B></CODE><BR> Applies the <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">libsvm</a> learner by Chih-Chung Chang and Chih-Jen Lin.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/MyKLRLearner.html" title="class in com.rapidminer.operator.learner.functions.kernel">MyKLRLearner</A></B></CODE><BR> This is the Java implementation of <em>myKLR</em> by Stefan Rüping.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/RVMLearner.html" title="class in com.rapidminer.operator.learner.functions.kernel">RVMLearner</A></B></CODE><BR> Relevance Vector Machine (RVM) Learner.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.functions.kernel.evosvm"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/evosvm/package-summary.html">com.rapidminer.operator.learner.functions.kernel.evosvm</A></FONT></TH></TR></TABLE> <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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/evosvm/package-summary.html">com.rapidminer.operator.learner.functions.kernel.evosvm</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/evosvm/EvoSVM.html" title="class in com.rapidminer.operator.learner.functions.kernel.evosvm">EvoSVM</A></B></CODE><BR> This is a SVM implementation using an evolutionary algorithm (ES) to solve the dual optimization problem of a SVM.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/evosvm/PSOSVM.html" title="class in com.rapidminer.operator.learner.functions.kernel.evosvm">PSOSVM</A></B></CODE><BR> This is a SVM implementation using a particle swarm optimization (PSO) approach to solve the dual optimization problem of a SVM.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.functions.kernel.hyperhyper"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/hyperhyper/package-summary.html">com.rapidminer.operator.learner.functions.kernel.hyperhyper</A></FONT></TH></TR></TABLE> <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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/hyperhyper/package-summary.html">com.rapidminer.operator.learner.functions.kernel.hyperhyper</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/kernel/hyperhyper/HyperHyper.html" title="class in com.rapidminer.operator.learner.functions.kernel.hyperhyper">HyperHyper</A></B></CODE><BR> This is a minimal SVM implementation.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.functions.neuralnet"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/neuralnet/package-summary.html">com.rapidminer.operator.learner.functions.neuralnet</A></FONT></TH></TR></TABLE> <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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/functions/neuralnet/package-summary.html">com.rapidminer.operator.learner.functions.neuralnet</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/functions/neuralnet/NeuralNetLearner.html" title="class in com.rapidminer.operator.learner.functions.neuralnet">NeuralNetLearner</A></B></CODE><BR> This operator learns a model by means of a feed-forward neural network.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.igss"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/igss/package-summary.html">com.rapidminer.operator.learner.igss</A></FONT></TH></TR></TABLE> <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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/igss/package-summary.html">com.rapidminer.operator.learner.igss</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/igss/IteratingGSS.html" title="class in com.rapidminer.operator.learner.igss">IteratingGSS</A></B></CODE><BR> This operator implements the IteratingGSS algorithmus presented in the diploma thesis 'Effiziente Entdeckung unabhaengiger Subgruppen in grossen Datenbanken' at the Department of Computer Science, University of Dortmund.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.lazy"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/lazy/package-summary.html">com.rapidminer.operator.learner.lazy</A></FONT></TH></TR></TABLE> <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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/lazy/package-summary.html">com.rapidminer.operator.learner.lazy</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/AttributeBasedVotingLearner.html" title="class in com.rapidminer.operator.learner.lazy">AttributeBasedVotingLearner</A></B></CODE><BR> AttributeBasedVotingLearner is very lazy.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/DefaultLearner.html" title="class in com.rapidminer.operator.learner.lazy">DefaultLearner</A></B></CODE><BR> This learner creates a model, that will simply predict a default value for all examples, i.e. the average or median of the true labels (or the mode in case of classification) or a fixed specified value.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/KNNLearner.html" title="class in com.rapidminer.operator.learner.lazy">KNNLearner</A></B></CODE><BR> A k nearest neighbor implementation.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.rules"><!-- --></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/AbstractLearner.html" title="class in com.rapidminer.operator.learner">AbstractLearner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/rules/package-summary.html">com.rapidminer.operator.learner.rules</A></FONT></TH></TR></TABLE> <P>⌨️ 快捷键说明
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