List of usage examples for weka.core TechnicalInformationHandler interface-usage
From source file Bilbo.java
/**
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* 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 StemmerOverRide.java
/**
* ADDED BY RACHEL WARREN: This stemmer has been modified so that the constructor takes an
* arrray of strings which are words which will not be stemmed.
*
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* A stemmer based on the Lovins stemmer, described here:<br/>
From source file WrapperSubset.java
/**
* <!-- globalinfo-start --> WrapperSubsetEval:<br/>
* <br/>
* Evaluates attribute sets by using a learning scheme. Cross validation is used
* to estimate the accuracy of the learning scheme for a set of attributes.<br/>
* <br/>
From source file SMO.java
/**
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* 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.MyFarthestFirst.java
/**
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* Cluster data using the FarthestFirst algorithm.<br/>
* <br/>
* For more information see:<br/>
* <br/>
From source file ann.SingleLayerPerceptron.java
/** * * @author gifarikautsar */ public class SingleLayerPerceptron extends Classifier implements TechnicalInformationHandler, Sourcable, Serializable {
From source file cba.Apriori.java
/**
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* Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.<br/>
* The algorithm has an option to mine class association rules. It is adapted as explained in the second reference.<br/>
* <br/>
* For more information see:<br/>
From source file cerebro.Id3.java
/**
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* Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves may result in unclassified instances. For more information see: <br/>
* <br/>
* R. Quinlan (1986). Induction of decision trees. Machine Learning. 1(1):81-106.
* <p/>
From source file clusterer.HaritsDBScan.java
/**
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* Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported. More info:<br/>
* <br/>
* Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.
* <p/>