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NAME="skip-navbar_top"></A><!-- ========= END OF TOP NAVBAR ========= --><HR><H2>Package weka.classifiers.functions.supportVector</H2><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Class Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/CachedKernel.html" title="class in weka.classifiers.functions.supportVector">CachedKernel</A></B></TD><TD>Base class for RBFKernel and PolyKernel that implements a simple LRU.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/CheckKernel.html" title="class in weka.classifiers.functions.supportVector">CheckKernel</A></B></TD><TD>Class for examining the capabilities and finding problems with kernels.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/Kernel.html" title="class in weka.classifiers.functions.supportVector">Kernel</A></B></TD><TD>Abstract kernel.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/KernelEvaluation.html" title="class in weka.classifiers.functions.supportVector">KernelEvaluation</A></B></TD><TD>Class for evaluating Kernels.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/NormalizedPolyKernel.html" title="class in weka.classifiers.functions.supportVector">NormalizedPolyKernel</A></B></TD><TD>The normalized polynomial kernel.<br/> K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y) <p/> <!-- globalinfo-end --> <!-- options-start --> Valid options are: <p/></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/PolyKernel.html" title="class in weka.classifiers.functions.supportVector">PolyKernel</A></B></TD><TD>The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p <p/> <!-- globalinfo-end --> <!-- options-start --> Valid options are: <p/></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/RBFKernel.html" title="class in weka.classifiers.functions.supportVector">RBFKernel</A></B></TD><TD>The RBF kernel.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/RegOptimizer.html" title="class in weka.classifiers.functions.supportVector">RegOptimizer</A></B></TD><TD>Base class implementation for learning algorithm of SVMreg <!-- options-start --> Valid options are: <p/></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/RegSMO.html" title="class in weka.classifiers.functions.supportVector">RegSMO</A></B></TD><TD>Implementation of SMO for support vector regression as described in :<br/> <br/> A.J.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/RegSMOImproved.html" title="class in weka.classifiers.functions.supportVector">RegSMOImproved</A></B></TD><TD>Learn SVM for regression using SMO with Shevade, Keerthi, et al.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/SMOset.html" title="class in weka.classifiers.functions.supportVector">SMOset</A></B></TD><TD>Stores a set of integer of a given size.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../../weka/classifiers/functions/supportVector/StringKernel.html" title="class in weka.classifiers.functions.supportVector">StringKernel</A></B></TD><TD>Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].<br/> <br/> For more information, see<br/> <br/> Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.</TD></TR></TABLE> <P><DL></DL><HR><!-- ======= START OF BOTTOM NAVBAR ====== --><A NAME="navbar_bottom"><!-- --></A><A HREF="#skip-navbar_bottom" 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_bottom_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <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 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