List of usage examples for weka.core Randomizable interface-usage
From source file NewRPISCE.java
/**
* Abstract utility class for handling settings common to randomizable
* meta classifiers that build an ensemble in parallel from a single base
* learner.
*
* @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
From source file ai.BalancedRandomForest.java
/**
* This class implements a Balanced Random Forest classifier,
* it is an ensemble classifier of random trees where all classes
* have the same representation in the training process.
*
* <!-- globalinfo-start -->
From source file classifiers.mlp.MultilayerPerceptronCustom.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 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.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.walmart.productgenome.matching.models.EMSRandomForest.java
/**
<!-- globalinfo-start -->
* Class for constructing a forest of random trees.<br/>
* <br/>
* For more information see: <br/>
* <br/>
From source file ffnn.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 hr.irb.fastRandomForest.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 learning.DMRandomForest.java
/**
<!-- globalinfo-start -->
* Class for constructing a forest of random trees.<br/>
* <br/>
* For more information see: <br/>
* <br/>
From source file meka.classifiers.multilabel.BRq.java
/**
* BRq.java - Random Subspace ('quick') Version.
* Like BR, but randomly samples the attribute space for each binary model. Intended for use in an ensemble (but will work in a standalone fashion also).
* <br>
* See: Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. <i>Classifier Chains for Multi-label Classification</i>. Machine Learning Journal. Springer. Vol. 85(3), pp 333-359. (May 2011).
* <br>