Example usage for weka.classifiers Sourcable interface-usage

Introduction

In this page you can find the example usage for weka.classifiers Sourcable 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 ann.SingleLayerPerceptron.java

/**
 *
 * @author gifarikautsar
 */
public class SingleLayerPerceptron extends Classifier
        implements TechnicalInformationHandler, Sourcable, Serializable {

From source file boosting.classifiers.DecisionStumpWritable.java

/**
 <!-- globalinfo-start -->
 * Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or classification (based on entropy). Missing is treated as a separate value.
 * <p/>
 <!-- globalinfo-end -->
 *

From source file cerebro.Id3.java

/**
 <!-- globalinfo-start -->
 * Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves may result in unclassified instances. For more information see: <br/>
 * <br/>
 * R. Quinlan (1986). Induction of decision trees. Machine Learning. 1(1):81-106.
 * <p/>

From source file dewaweebtreeclassifier.veranda.VerandaTree.java

/**
 *
 * @author Ahmad
 */
public class VerandaTree extends AbstractClassifier implements Sourcable {

From source file gyc.OverBoostM1.java

/**
 <!-- globalinfo-start -->
 * Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.<br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file gyc.UnderOverBoostM1.java

/**
 <!-- globalinfo-start -->
 * Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.<br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file id3.MyID3.java

/**
 *
 * @author rikysamuel
 */
public class MyID3 extends Classifier implements TechnicalInformationHandler, Sourcable {
    private Attribute currentAttribute;

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.

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