Example usage for weka.core WeightedInstancesHandler interface-usage

Introduction

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

Usage

From source file WLSVM.java

public class WLSVM extends weka.classifiers.AbstractClassifier implements WeightedInstancesHandler {

    protected static final long serialVersionUID = 14172;

    protected svm_parameter param; // LibSVM oprions

From source file Bilbo.java

/**
 <!-- globalinfo-start -->
 * Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. <br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file BaggingImprove.java

/**
 *
 * @author sartikahasibuan
 */
public class BaggingImprove extends RandomizableIteratedSingleClassifierEnhancer
        implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler {

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 CJWeka.java

public class CJWeka extends AbstractClassifier implements CJProxy, OptionHandler, WeightedInstancesHandler {

    /** for serialization */
    private static final long serialVersionUID = -4393145704384476775L;

    private static Instances ii;

From source file SMO.java

/**
 <!-- globalinfo-start -->
 * 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. It also normalizes all attributes by default. (In that case the coefficients in the output are based on the normalized data, not the original data --- this is important for interpreting the classifier.)<br/>
 * <br/>

From source file adaptedClusteringAlgorithms.MySimpleKMeans.java

/**
 * <!-- globalinfo-start --> Cluster data using the k means algorithm
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:

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 br.ufrn.ia.core.clustering.EMIaProject.java

public class EMIaProject extends RandomizableDensityBasedClusterer
        implements NumberOfClustersRequestable, WeightedInstancesHandler {

    static final long serialVersionUID = 8348181483812829475L;

    private static double m_normConst = Math.log(Math.sqrt(2 * Math.PI));