Example usage for weka.core Drawable interface-usage

Introduction

In this page you can find the example usage for weka.core Drawable interface-usage.

Usage

From source file REPTree.java

/**
 <!-- globalinfo-start -->
 * Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).  Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
 * <p/>
 <!-- globalinfo-end -->
 *

From source file REPRandomTree.java

/**
 <!-- globalinfo-start -->
 * Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).  Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
 * <p/>
 <!-- globalinfo-end -->
 *

From source file HierarchicalClusterer.java

/**
<!-- globalinfo-start -->
* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
<!-- globalinfo-end -->
* 

From source file classes.AbdoAgglomerativeClusterer.java

/**
 *
 * @author Eng.Abdo
 */
public class AbdoAgglomerativeClusterer extends AbstractClusterer
        implements OptionHandler, CapabilitiesHandler, Drawable {

From source file de.uni_potsdam.hpi.bpt.promnicat.analysisModules.clustering.HierarchicalProcessClusterer.java

/**
 * Extends WEKAs {@link HierarchicalClusterer} by creating a clustertree
 * directly containing the clustered elements with the feature vector and
 * process models. Also, this clusterer can cluster both numeric and string
 * values at the same time and uses weights for clustering.
 * 

From source file j48.ClassifierTree.java

/**
 * Class for handling a tree structure used for
 * classification.
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 5531 $

From source file j48.J48.java

/**
 * <!-- globalinfo-start --> Class for generating a pruned or unpruned C4.5
 * decision tree. For more information, see<br/>
 * <br/>
 * Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann
 * Publishers, San Mateo, CA.

From source file library.MikeJ48.java

/**
 <!-- globalinfo-start -->
 * Class for generating a pruned or unpruned C4.5 decision tree. For more information, see<br/>
 * <br/>
 * Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
 * <p/>

From source file LogReg.FilteredLogRegClassifier.java

/**
 <!-- globalinfo-start -->
 * Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.
 * <p/>
 <!-- globalinfo-end -->
 *

From source file org.fabrelab.textkit.tools.cluster.HierarchicalClustererWithThreshold.java

/**
<!-- globalinfo-start -->
* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
<!-- globalinfo-end -->
* 

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