List of usage examples for weka.classifiers AbstractClassifier subclass-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