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LearnParam (JNI_SVM-light-6.01 - A Java Native Interface (JNI) for SVM-light v6.01)
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jnisvmlight</FONT>
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Class LearnParam</H2>
<PRE>
java.lang.Object
<IMG SRC="../resources/inherit.gif" ALT="extended by "><B>jnisvmlight.LearnParam</B>
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<DT><PRE>public class <A HREF="../src-html/jnisvmlight/LearnParam.html#line.29"><B>LearnParam</B></A><DT>extends java.lang.Object</DL>
</PRE>
<P>
Learning parameters as denoted by SVM-light.
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<B>Field Summary</B></FONT></TH>
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<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> java.lang.String</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#alphafile">alphafile</A></B></CODE>
<BR>
File to store optimal alphas in.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#argc">argc</A></B></CODE>
<BR>
The cardinality of the command line parameters.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> java.lang.String[]</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#argv">argv</A></B></CODE>
<BR>
Optionally simulates a simple command shell-like usage and transfers the command line parameters to SVM-light.</TD>
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<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#biased_hyperplane">biased_hyperplane</A></B></CODE>
<BR>
If nonzero, use hyperplane w*x+b=0 otherwise w*x=0.</TD>
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<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE>static int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#CLASSIFICATION">CLASSIFICATION</A></B></CODE>
<BR>
Trains a classification model.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#compute_loo">compute_loo</A></B></CODE>
<BR>
If nonzero, computes leave-one-outestimates.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#eps">eps</A></B></CODE>
<BR>
Regression epsilon (eps=1.0 for classification).</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#epsilon_a">epsilon_a</A></B></CODE>
<BR>
Tolerable error on alphas at bounds.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#epsilon_const">epsilon_const</A></B></CODE>
<BR>
Tolerable error on eq-constraint.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#epsilon_crit">epsilon_crit</A></B></CODE>
<BR>
Tolerable error for distances used in stopping criterion.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#epsilon_shrink">epsilon_shrink</A></B></CODE>
<BR>
How much a multiplier should be above zero for shrinking.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#kernel_cache_size">kernel_cache_size</A></B></CODE>
<BR>
Size of kernel cache in megabytes.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#maxiter">maxiter</A></B></CODE>
<BR>
Number of iterations after which the optimizer terminates, if there was no progress in maxdiff.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#opt_precision">opt_precision</A></B></CODE>
<BR>
Precision of solver, set to e.g.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE>static int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#OPTIMIZATION">OPTIMIZATION</A></B></CODE>
<BR>
Trains on general set of constraints.</TD>
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<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> java.lang.String</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#predfile">predfile</A></B></CODE>
<BR>
File for predicitions on unlabeled examples in transduction.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE>static int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#RANKING">RANKING</A></B></CODE>
<BR>
Trains a ranking model.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE>static int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#REGRESSION">REGRESSION</A></B></CODE>
<BR>
Trains a regression model.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#remove_inconsistent">remove_inconsistent</A></B></CODE>
<BR>
Exclude examples with alpha at C and retrain.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#rho">rho</A></B></CODE>
<BR>
Parameter in xi/alpha-estimates and for pruning leave-one-out range [1..2].</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#sharedslack">sharedslack</A></B></CODE>
<BR>
If nonzero, it will use the shared slack variable mode.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#skip_final_opt_check">skip_final_opt_check</A></B></CODE>
<BR>
Do not check KT-Conditions at the end of optimization for examples removed by shrinking.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#svm_c">svm_c</A></B></CODE>
<BR>
Upper bound C on alphas.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#svm_c_factor">svm_c_factor</A></B></CODE>
<BR>
Increase C by this factor every step.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> long</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#svm_c_steps">svm_c_steps</A></B></CODE>
<BR>
Do so many steps for finding optimal C.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#svm_cost">svm_cost</A></B></CODE>
<BR>
Individual upper bounds for each var.</TD>
</TR>
<TR BGCOLOR="white" CLASS="TableRowColor">
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../jnisvmlight/LearnParam.html#svm_costratio">svm_costratio</A></B></CODE>
<BR>
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