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weka.classifiers.functions</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/GaussianProcesses.html" title="class in weka.classifiers.functions">GaussianProcesses</A></B></TD><TD>Implements Gaussian Processes for regression without hyperparameter-tuning.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/IsotonicRegression.html" title="class in weka.classifiers.functions">IsotonicRegression</A></B></TD><TD>Learns an isotonic regression model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/LeastMedSq.html" title="class in weka.classifiers.functions">LeastMedSq</A></B></TD><TD>Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/LibSVM.html" title="class in weka.classifiers.functions">LibSVM</A></B></TD><TD>A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).<br/> LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.<br/> LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/LinearRegression.html" title="class in weka.classifiers.functions">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="../../../weka/classifiers/functions/Logistic.html" title="class in weka.classifiers.functions">Logistic</A></B></TD><TD>Class for building and using a multinomial logistic regression model with a ridge estimator.<br/> <br/> There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): <br/> <br/> If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.<br/> <br/> The probability for class j with the exception of the last class is<br/> <br/> Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) <br/> <br/> The last class has probability<br/> <br/> 1-(sum[j=1..(k-1)]Pj(Xi)) <br/> = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)<br/> <br/> The (negative) multinomial log-likelihood is thus: <br/> <br/> L = -sum[i=1..n]{<br/> sum[j=1..(k-1)](Yij * ln(Pj(Xi)))<br/> +(1 - (sum[j=1..(k-1)]Yij)) <br/> * ln(1 - sum[j=1..(k-1)]Pj(Xi))<br/> } + ridge * (B^2)<br/> <br/> In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html" title="class in weka.classifiers.functions">MultilayerPerceptron</A></B></TD><TD>A Classifier that uses backpropagation to classify instances.<br/> This network can be built by hand, created by an algorithm or both.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/PaceRegression.html" title="class in weka.classifiers.functions">PaceRegression</A></B></TD><TD>Class for building pace regression linear models and using them for prediction.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/PLSClassifier.html" title="class in weka.classifiers.functions">PLSClassifier</A></B></TD><TD>A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/RBFNetwork.html" title="class in weka.classifiers.functions">RBFNetwork</A></B></TD><TD>Class that implements a normalized Gaussian radial basisbasis function network.<br/> It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/SimpleLinearRegression.html" title="class in weka.classifiers.functions">SimpleLinearRegression</A></B></TD><TD>Learns a simple linear regression model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/SimpleLogistic.html" title="class in weka.classifiers.functions">SimpleLogistic</A></B></TD><TD>Classifier for building linear logistic regression models.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/SMO.html" title="class in weka.classifiers.functions">SMO</A></B></TD><TD>Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.<br/> <br/> This implementation globally replaces all missing values and transforms nominal attributes into binary ones.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/SMOreg.html" title="class in weka.classifiers.functions">SMOreg</A></B></TD><TD>Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/SVMreg.html" title="class in weka.classifiers.functions">SVMreg</A></B></TD><TD>SVMreg implements the support vector machine for regression.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/VotedPerceptron.html" title="class in weka.classifiers.functions">VotedPerceptron</A></B></TD><TD>Implementation of the voted perceptron algorithm by Freund and Schapire.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/functions/Winnow.html" title="class in weka.classifiers.functions">Winnow</A></B></TD><TD>Implements Winnow and Balanced Winnow algorithms by Littlestone.<br/> <br/> For more information, see<br/> <br/> N.</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 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