Example usage for weka.classifiers AbstractClassifier subclass-usage

List of usage examples 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 ANN_Single.SinglelayerPerceptron.java

/**
 *
 * @author Mujahid Suriah
 */
public class SinglelayerPerceptron extends AbstractClassifier {
    ArrayList<Node> listOutput;

From source file ANN_single2.MultilayerPerceptron.java

/**
 *
 * @author Mujahid Suriah
 */
public class MultilayerPerceptron extends AbstractClassifier {
    ArrayList<Node> listHidden;

From source file ANN_single2.SinglelayerPerceptron.java

/**
 *
 * @author Mujahid Suriah
 */
public class SinglelayerPerceptron extends AbstractClassifier {
    ArrayList<Node> listOutput;

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 Classifier.supervised.LinearRegression.java

/**
 <!-- globalinfo-start -->
 * Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
 * <p/>
 <!-- globalinfo-end -->
 *

From source file com.actelion.research.orbit.imageAnalysis.models.ThresholdClassifier.java

public class ThresholdClassifier extends AbstractClassifier {

    private static final long serialVersionUID = 1L;
    private double[] mins = null;
    private double[] maxs = null;

From source file com.ifmo.recommendersystem.metafeatures.classifierbased.internal.extractors.MultilayerPerceptron.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

From source file com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java

/**
 * <!-- globalinfo-start -->
 * A simple meta-classifier that uses a clusterer for classification.
 * By default, the best single cluster for each class is found using the method Weka
 * applies for classes-to-clusters evaluation. All other clusters are left without
 * class labels and a test instance assigned to one of the unlabeled clusters is left

From source file com.tum.classifiertest.FastRandomForest.java

/**
 * Based on the "weka.classifiers.trees.RandomForest" class, revision 1.12,
 * by Richard Kirkby, with minor modifications:
 * <p/>
 * - uses FastRfBagger with FastRandomTree, instead of Bagger with RandomTree.
 * - stores dataset header (instead of every Tree storing its own header)

From source file com.tum.classifiertest.FastRandomTree.java

/**
 * Based on the "weka.classifiers.trees.RandomTree" class, revision 1.19,
 * by Eibe Frank and Richard Kirkby, with major modifications made to improve
 * the speed of classifier training.
 * 
 * Please refer to the Javadoc of buildTree, splitData and distribution