Example usage for weka.classifiers UpdateableClassifier interface-usage

Introduction

In this page you can find the example usage for weka.classifiers UpdateableClassifier interface-usage.

Usage

From source file AnDE.wdAnDEonline.java

public class wdAnDEonline extends AbstractClassifier implements OptionHandler, UpdateableClassifier {

    private static final long serialVersionUID = 4823531716976859217L;

    private Instances m_Instances;

From source file com.reactivetechnologies.platform.analytics.core.IncrementalClassifierBean.java

public class IncrementalClassifierBean extends Classifier implements UpdateableClassifier, Regression {

    private static final Logger log = LoggerFactory.getLogger(IncrementalClassifierBean.class);

    private final Classifier clazzifier;
    private ExecutorService thread;

From source file meka.classifiers.multilabel.IncrementalMultiLabelClassifier.java

/**
 * Interface for incremental multi-label classifiers.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */

From source file net.sf.jclal.classifier.MOAWrapper.java

public class MOAWrapper extends weka.classifiers.AbstractClassifier implements UpdateableClassifier, IConfigure {

    /** for serialization. */
    private static final long serialVersionUID = 2605797948130310166L;

    /** the actual MOA classifier to use for learning. */

From source file org.esa.nest.gpf.SGD.java

/**
 * <!-- globalinfo-start -->
 * Implements stochastic gradient descent for learning various linear models
 * (binary class SVM, binary class logistic regression and linear regression).
 * Globally replaces all missing values and transforms nominal attributes into
 * binary ones. It also normalizes all attributes, so the coefficients in the

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

/**
 * 
 * @author hornm, University of Konstanz
 */
public class IntervalRule extends AbstractClassifier implements UpdateableClassifier {

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 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.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/>