Example usage for weka.classifiers AbstractClassifier subclass-usage

Introduction

In this page you can find the example usage for weka.classifiers AbstractClassifier subclass-usage.

Usage

From source file org.knime.knip.suise.node.boundarymodel.contourdata.IRI.java

/**
 * Interval Rule Induction
 * 
 * TODO's: contour models (consisting of different rules)
 * 
 * @author hornm, University of Konstanz

From source file org.wkwk.classifier.MyC45.java

/**
 *
 * @author adarwawan
 */
public class MyC45 extends AbstractClassifier {

From source file test.org.moa.opencl.IBk.java

/**
 <!-- globalinfo-start -->
 * K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting.<br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file tr.gov.ulakbim.jDenetX.classifiers.WEKAClassifier.java

public class WEKAClassifier extends AbstractClassifier {

    private static final long serialVersionUID = 1L;

    public WEKAClassOption baseLearnerOption = new WEKAClassOption("baseLearner", 'l', "Classifier to train.",
            weka.classifiers.Classifier.class, "weka.classifiers.bayes.NaiveBayesUpdateable");

From source file uzholdem.classifier.OnlineMultilayerPerceptron.java

/** 
 <!-- globalinfo-start -->
 * A Classifier that uses backpropagation to classify instances.<br/>
 * This network can be built by hand, created by an algorithm or both. The network can also be monitored and modified during training time. The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units).
 * <p/>
 <!-- globalinfo-end -->

From source file uzholdem.classifier.UpdateableMultilayerPerceptron.java

/** 
 <!-- globalinfo-start -->
 * A Classifier that uses backpropagation to classify instances.<br/>
 * This network can be built by hand, created by an algorithm or both. The network can also be monitored and modified during training time. The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units).
 * <p/>
 <!-- globalinfo-end -->

From source file xlong.urlclassify.others.LibLINEAR.java

/**
  <!-- globalinfo-start -->
  * A wrapper class for the liblinear classifier.<br/>
  * Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.
  * <p/>
  <!-- globalinfo-end -->

From source file xlong.urlclassify.others.SPegasos.java

/**
 <!-- globalinfo-start -->
 * Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. For more information, see<br/>
 * <br/>
 * S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
 * <p/>