Name | Description | Type | Package | Framework |
AbstractBayesianClassifier | Abstract Bayesian classifier (supervised). | Class | net.sf.javaml.classification.bayes | JavaML |
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AbstractBayesianClassifier_compact | Compact Abstract Bayesian classifier (supervised). | Class | net.sf.javaml.classification.bayes | JavaML |
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AbstractClassifier | | Class | net.sf.javaml.classification | JavaML |
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AbstractCorrelation | Abstract super class for all correlation measures. | Class | net.sf.javaml.distance | JavaML |
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AbstractDistance | Abstract super class for all distance measures. | Class | net.sf.javaml.distance | JavaML |
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AbstractFilter | Umbrella class for filters that implements both the DatasetFilter interfaces. | Class | net.sf.javaml.filter | JavaML |
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AbstractInstance | | Class | net.sf.javaml.core | JavaML |
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Abstraction | | Class | net.sf.javaml.distance.fastdtw | JavaML |
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AbstractMeanClassifier | Abstract classifier class that is the parent of all classifiers that require the mean of each class as training. | Class | net.sf.javaml.classification | JavaML |
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AbstractSimilarity | Abstract super class for all similarity measures. | Class | net.sf.javaml.distance | JavaML |
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ActiveSetsOptimization | problem with only bounds constraints in multi-dimensions. | Class | net.sf.javaml.utils | JavaML |
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AICScore | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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AngularDistance | | Class | net.sf.javaml.distance | JavaML |
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AQBC | This class implements the Adaptive Quality-based Clustering Algorithm, based on the implementation in MATLAB by De Smet et al. | Class | net.sf.javaml.clustering | JavaML |
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ARFFHandler | Provides method to load data from ARFF formatted files. | Class | net.sf.javaml.tools.data | JavaML |
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Arrays | | Class | net.sf.javaml.distance.fastdtw.util | JavaML |
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ArrayUtils | | Class | net.sf.javaml.utils | JavaML |
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Bagging | Bagging meta learner. | Class | net.sf.javaml.classification.meta | JavaML |
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Band | | Class | net.sf.javaml.distance.fastdtw | JavaML |
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BICScore | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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CachedDistance | This class implements a wrapper around other distance measure to cache previously calculated distances. | Class | net.sf.javaml.distance | JavaML |
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CeilValueFilter | Filter to replace all values with their ceiled equivalent. | Class | net.sf.javaml.filter.instance | JavaML |
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ChebychevDistance | | Class | net.sf.javaml.distance | JavaML |
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CIndex | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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ClassCounter | Data structure used for Bayesian networksAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.classification.bayes | JavaML |
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ClassCounter_compact | Data structure used for Entropy based algorithmsAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.classification.bayes | JavaML |
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Classifier | Interface for all classifiers. | Interface | net.sf.javaml.classification | JavaML |
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ClassRemoveFilter | Removes all instances from a data set that have a specific class valueVersion:0. | Class | net.sf.javaml.filter | JavaML |
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ClassReplaceFilter | Replaces a certain class value with another one. | Class | net.sf.javaml.filter | JavaML |
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ClassRetainFilter | Keeps all instances from a data set that have a specific class valueVersion:0. | Class | net.sf.javaml.filter | JavaML |
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Clusterer | A common interface for all clustering techniques. | Interface | net.sf.javaml.clustering | JavaML |
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ClusterEvaluation | This interface provides a frame for all measure that can be used to evaluate the quality of a clusterer. | Interface | net.sf.javaml.clustering.evaluation | JavaML |
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Cobweb | Class implementing the Cobweb and Classit clustering algorithms. | Class | net.sf.javaml.clustering | JavaML |
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ColMajorCell | | Class | net.sf.javaml.distance.fastdtw.matrix | JavaML |
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Complex | | Class | net.sf.javaml.core | JavaML |
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ConsistencyIndex | Consistency index for a pair of subsets. | Class | net.sf.javaml.distance | JavaML |
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ContingencyTables | Class implementing some statistical routines for contingency tables. | Class | net.sf.javaml.utils | JavaML |
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CosineDistance | This similarity based distance measure actually measures the angle between The value returned lies in the interval [0,1]. | Class | net.sf.javaml.distance | JavaML |
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CosineSimilarity | This similarity based distance measure actually measures the angle between The value returned lies in the interval [0,1]. | Class | net.sf.javaml.distance | JavaML |
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CrossValidation | | Class | net.sf.javaml.classification.evaluation | JavaML |
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Dataset | Interface for a data set. | Interface | net.sf.javaml.core | JavaML |
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DatasetFilter | The interface for filters that can be applied on an When applying a filter to a data set it may modify the instances in the | Interface | net.sf.javaml.filter | JavaML |
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DatasetTools | This class provides utility methods on data sets. | Class | net.sf.javaml.tools | JavaML |
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DefaultDataset | Provides a standard data set implementation. | Class | net.sf.javaml.core | JavaML |
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DenseInstance | double array that provides a value for each attribute index. | Class | net.sf.javaml.core | JavaML |
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DensityBasedSpatialClustering | Provides the density-based-spatial-scanning clustering algorithm. | Class | net.sf.javaml.clustering | JavaML |
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DistanceMeasure | A distance measure is an algorithm to calculate the distance, similarity or correlation between two instances. | Interface | net.sf.javaml.distance | JavaML |
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DoubleFormat | DoubleFormat formats double numbers into a specified digit format. | Class | net.sf.javaml.clustering.mcl | JavaML |
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DTW | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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DTWSimilarity | A similarity measure based on "Dynamic Time Warping". | Class | net.sf.javaml.distance.dtw | JavaML |
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EqualWidthBinning | A filter that discretizes a range of numeric attributes in the data set into nominal attributes. | Class | net.sf.javaml.filter.discretize | JavaML |
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EuclideanDistance | This class implements the Euclidean distance. | Class | net.sf.javaml.distance | JavaML |
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EvaluateDataset | Tests a classifier on a data setAuthor:Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.classification.evaluation | JavaML |
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ExpandedResWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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ExpDouble | ExpDouble represents a double-precision number by a mantissa, a decimal exponent and the number of digits in the mantissa, in order to allow | Class | net.sf.javaml.clustering.mcl | JavaML |
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FarthestFirst | Cluster data using the FarthestFirst algorithm. | Class | net.sf.javaml.clustering | JavaML |
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FastDTW | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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FastDTW | Stan Salvador and Philip Chan, FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space, KDD Workshop on Mining Temporal and Sequential | Class | net.sf.javaml.distance.fastdtw | JavaML |
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FeatureRanking | Interface for algorithms that can generate an attribute ranking. | Interface | net.sf.javaml.featureselection | JavaML |
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FeatureScoring | Interface for all attribute evaluation methods. | Interface | net.sf.javaml.featureselection | JavaML |
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FeatureSelection | Top-level interface for feature selection algorithms. | Interface | net.sf.javaml.featureselection | JavaML |
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FeatureSubsetSelection | Interface for all attribute subset selection algorithms. | Interface | net.sf.javaml.featureselection | JavaML |
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FileHandler | A class to load data sets from file and write them back. | Class | net.sf.javaml.tools.data | JavaML |
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FloorValueFilter | Filter to replace all values with their rounded equivalentAuthor:Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.filter.instance | JavaML |
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FourBinMinimalEntropyPartitioning | A filter that discretizes a range of numeric attributes in the data set into 4 nominal attributes. | Class | net.sf.javaml.filter.discretize | JavaML |
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FromWekaUtils | Provides utility methods to convert data from the WEKA format to the Java-MLVersion:0. | Class | net.sf.javaml.tools.weka | JavaML |
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FullWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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GainRatio | | Class | net.sf.javaml.featureselection.scoring | JavaML |
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Gamma | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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GammaFunction | | Class | net.sf.javaml.utils | JavaML |
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GPlus | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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GreedyBackwardElimination | Provides an implementation of the backward greedy attribute subset elimination algorithm. | Class | net.sf.javaml.featureselection.subset | JavaML |
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GreedyForwardSelection | Provides an implementation of the forward greedy attribute subset selection. | Class | net.sf.javaml.featureselection.subset | JavaML |
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GridSearch | Helps finding optimal parameters C and gamma for the LibSVM Support Vector Machine. | Class | libsvm | JavaML |
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HybridCentroidSimilarity | H_2 from the Zhao 2001 paperAuthor:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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HybridPairwiseSimilarities | H_1 from the Zhao 2001 paperAuthor:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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Instance | The interface for instances in a data set. | Interface | net.sf.javaml.core | JavaML |
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InstanceFilter | The interface for filters that can be applied on an Instance without the need for a reference | Interface | net.sf.javaml.filter | JavaML |
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InstanceNormalizeMidrange | This filter will normalize all the attributes in an instance to a certain interval determined by a mid-range and a range. | Class | net.sf.javaml.filter.normalize | JavaML |
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InstanceTools | Provides utility methods for manipulating, creating and modifying instances. | Class | net.sf.javaml.tools | JavaML |
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IterativeFarthestFirst | | Class | net.sf.javaml.clustering | JavaML |
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IterativeKMeans | This class implements an extension of KMeans. | Class | net.sf.javaml.clustering | JavaML |
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IterativeMultiKMeans | This class implements an extension of KMeans, combining Iterative- en MultiKMeans. | Class | net.sf.javaml.clustering | JavaML |
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JaccardIndexDistance | Jaccard index. | Class | net.sf.javaml.distance | JavaML |
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JaccardIndexSimilarity | Jaccard index. | Class | net.sf.javaml.distance | JavaML |
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KDependentBayesClassifier | | Class | net.sf.javaml.classification.bayes | JavaML |
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KDTree | KDTree is a class supporting KD-tree insertion, deletion, equality search, range search, and nearest neighbor(s) using double-precision floating-point | Class | net.sf.javaml.core.kdtree | JavaML |
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KDtreeKNN | KDtree support. | Class | net.sf.javaml.classification | JavaML |
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KMeans | J. | Class | net.sf.javaml.clustering | JavaML |
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KMedoids | algorithm that is very much like k-means. | Class | net.sf.javaml.clustering | JavaML |
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KNearestNeighbors | | Class | net.sf.javaml.classification | JavaML |
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KNearestNeighbors | Replaces the missing value with the average of the values of its nearest This technique does not guarantee that all missing will be replaced. | Class | net.sf.javaml.filter.missingvalue | JavaML |
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KullbackLeiblerDivergence | Feature scoring algorithm based on Kullback-Leibler divergence of the value distributions of features. | Class | net.sf.javaml.featureselection.scoring | JavaML |
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LibSVM | Wrapper for the libSVM library by Chih-Chung Chang and Chih-Jen Lin. | Class | libsvm | JavaML |
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LinearKernel | | Class | net.sf.javaml.distance | JavaML |
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LinearRankingEnsemble | Provides a linear aggregation feature selection ensemble as described in Saeys, Y. | Class | net.sf.javaml.featureselection.ensemble | JavaML |
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LinearWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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ListTools | | Class | net.sf.javaml.tools | JavaML |
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LogLikelihoodFunction | | Class | net.sf.javaml.utils | JavaML |
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MahalanobisDistance | | Class | net.sf.javaml.distance | JavaML |
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ManhattanDistance | The Manhattan distance is the sum of the (absolute) differences of their coordinates. | Class | net.sf.javaml.distance | JavaML |
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MarkovClustering | MarkovClustering implements the Markov clustering (MCL) algorithm for graphs, using a HashMap-based sparse representation of a Markov matrix, i. | Class | net.sf.javaml.clustering.mcl | JavaML |
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MathUtils | A class that provides some utility math methods. | Class | net.sf.javaml.utils | JavaML |
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Matrix | | Class | net.sf.javaml.matrix | JavaML |
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MaxProductSimilarity | Specialized similarity that takes the maximum product of two feature values. | Class | net.sf.javaml.distance | JavaML |
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MCL | | Class | net.sf.javaml.clustering.mcl | JavaML |
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MeanFeatureVotingClassifier | This classifier calculates the mean for each class. | Class | net.sf.javaml.classification | JavaML |
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MinkowskiDistance | | Class | net.sf.javaml.distance | JavaML |
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MinMaxCut | G_1 from the Zhao 2001 paperAuthor:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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MissingClassFilter | Filters all instances from a data set that have their class value not setVersion:0. | Class | net.sf.javaml.filter | JavaML |
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MultiKMeans | This class implements an extension of KMeans (SKM). | Class | net.sf.javaml.clustering | JavaML |
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NaiveBayesClassifier | | Class | net.sf.javaml.classification.bayes | JavaML |
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NearestMeanClassifier | Nearest mean classifier. | Class | net.sf.javaml.classification | JavaML |
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NormalizedEuclideanDistance | A normalized version of the Euclidean distance. | Class | net.sf.javaml.distance | JavaML |
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NormalizedEuclideanSimilarity | | Class | net.sf.javaml.distance | JavaML |
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NormalizeMean | This filter will normalize the data set with mean 0 and standard deviation 1 The normalization will be done on the attributes, so each attribute will have | Class | net.sf.javaml.filter.normalize | JavaML |
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NormalizeMeanIQR135 | This filter will normalize the data set with mean 0 and standard deviation 1 The normalization will be done on the attributes, so each attribute will have | Class | net.sf.javaml.filter.normalize | JavaML |
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NormalizeMidrange | This filter will normalize the data set with a certain mid-range and a certain range for each attribute. | Class | net.sf.javaml.filter.normalize | JavaML |
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NormDistance | The norm distance or This class implements the Norm distance. | Class | net.sf.javaml.distance | JavaML |
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PAA | | Class | net.sf.javaml.distance.fastdtw.timeseries | JavaML |
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ParallelogramWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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PearsonCorrelationCoefficient | Calculates the Pearson Correlation Coeffient between two vectors. | Class | net.sf.javaml.distance | JavaML |
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PerformanceMeasure | Class implementing several performance measures commonly used for classification algorithms. | Class | net.sf.javaml.classification.evaluation | JavaML |
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PointBiserial | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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Polynomial | Polynomial functions. | Class | net.sf.javaml.utils | JavaML |
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PolynomialKernel | | Class | net.sf.javaml.distance | JavaML |
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RandomForest | | Class | net.sf.javaml.classification.tree | JavaML |
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RandomForestAttributeEvaluation | Random Forest based attribute evaluation. | Class | net.sf.javaml.featureselection.scoring | JavaML |
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RandomTree | Simple and fast implementation of the RandomTree classifier. | Class | net.sf.javaml.classification.tree | JavaML |
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RankingFromScoring | Creates an attribute ranking from an attribute evaluation technique. | Class | net.sf.javaml.featureselection.ranking | JavaML |
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RBFKernel | The kernel method for measuring similarities between instances. | Class | net.sf.javaml.distance | JavaML |
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RBFKernelDistance | | Class | net.sf.javaml.distance | JavaML |
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RecursiveFeatureEliminationSVM | Starting with the full feature set, attributes are ranked according to the weights they get in a linear SVM. | Class | net.sf.javaml.featureselection.ranking | JavaML |
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RecursiveMinimalEntropyPartitioning | A filter that discretizes a range of numeric attributes in the data set into nominal attributes. | Class | net.sf.javaml.filter.discretize | JavaML |
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RELIEF | This implementation is extended to include more neighbors in calculating the weights of the features. | Class | net.sf.javaml.featureselection.scoring | JavaML |
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RemoveAttributes | | Class | net.sf.javaml.filter | JavaML |
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RemoveMissingValue | Removes all instances that have missing values. | Class | net.sf.javaml.filter.missingvalue | JavaML |
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ReplaceValueFilter | | Class | net.sf.javaml.filter.instance | JavaML |
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ReplaceWithValue | Replaces all Double. | Class | net.sf.javaml.filter.missingvalue | JavaML |
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RetainAttributes | Filter to retain a set of wanted attributes and remove all othersAuthor:Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.filter | JavaML |
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RoundValueFilter | Filter to replace all values with their rounded equivalentAuthor:Thomas Abeel (thomas@abeel. | Class | net.sf.javaml.filter.instance | JavaML |
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Sampling | | Class | net.sf.javaml.sampling | JavaML |
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SamplingMethod | Defines sampling methods to select a subset of a set integers. | Class | net.sf.javaml.sampling | JavaML |
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SearchWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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SelfOptimizingLinearLibSVM | A svm variant the optimizes the C-paramater by itself. | Class | libsvm | JavaML |
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Serial | Class with utility methods for serialization. | Class | net.sf.javaml.tools | JavaML |
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SetTools | | Class | net.sf.javaml.tools | JavaML |
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SimpleBagging | Bootstrap aggregating (Bagging) meta learner. | Class | net.sf.javaml.classification.meta | JavaML |
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SineWave | | Class | net.sf.javaml.distance.fastdtw.timeseries | JavaML |
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SOM | Classifier based on the Self-organized map clustering. | Class | net.sf.javaml.classification | JavaML |
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SOM | An implementation of the Self Organizing Maps algorithm as proposed by This implementation is derived from the Bachelor thesis of Tomi Suuronen | Class | net.sf.javaml.clustering | JavaML |
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SparseInstance | indices to values. | Class | net.sf.javaml.core | JavaML |
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SparseMatrix | SparseMatrix is a sparse matrix with row-major format. | Class | net.sf.javaml.clustering.mcl | JavaML |
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SparseVector | SparseVector represents a sparse vector. | Class | net.sf.javaml.clustering.mcl | JavaML |
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SpearmanFootruleDistance | | Class | net.sf.javaml.distance | JavaML |
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SpearmanRankCorrelation | Calculates the Spearman rank correlation of two instances. | Class | net.sf.javaml.distance | JavaML |
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SpecialFunctions | Class implementing some mathematical functions. | Class | net.sf.javaml.utils | JavaML |
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Statistics | Class implementing some distributions, tests, etc. | Class | net.sf.javaml.utils | JavaML |
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StreamHandler | | Class | net.sf.javaml.tools.data | JavaML |
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SumOfAveragePairwiseSimilarities | I_1 from the Zhao 2001 paperAuthor:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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SumOfCentroidSimilarities | TODO uitleg I_2 from Zhao 2001Author:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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SumOfSquaredErrors | I_3 from the Zhao 2001 paperAuthor:Andreas De Rijcke | Class | net.sf.javaml.clustering.evaluation | JavaML |
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SymmetricalUncertainty | | Class | net.sf.javaml.featureselection.scoring | JavaML |
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Tau | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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ThreeBinMinimalEntropyPartitioning | A filter that discretizes a range of numeric attributes in the data set into 3 nominal attributes. | Class | net.sf.javaml.filter.discretize | JavaML |
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TimeSeries | | Class | net.sf.javaml.distance.fastdtw.timeseries | JavaML |
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TimeSeriesPoint | | Class | net.sf.javaml.distance.fastdtw.timeseries | JavaML |
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TimeWarpInfo | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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ToWekaUtils | Provides utility methods to convert data to the WEKA format. | Class | net.sf.javaml.tools.weka | JavaML |
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TraceScatterMatrix | E_1 from the Zhao 2001 paper Distance measure has to be | Class | net.sf.javaml.clustering.evaluation | JavaML |
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TrainingRequiredException | Indicates that the algorithm that throws this exception should have been trained prior to point the exception was thrown. | Class | net.sf.javaml.core.exception | JavaML |
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Tutorial2BinMinimalEntropyPartitioning | Tutorial Two Bin Minimal Entropy PartitioningAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.filter | JavaML |
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Tutorial3BinMinimalEntropyPartitioning | Tutorial Three Bin Minimal Entropy PartitioningAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.filter | JavaML |
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Tutorial4BinMinimalEntropyPartitioning | Tutorial Four Bin Minimal Entropy PartitioningAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.filter | JavaML |
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TutorialARFFLoader | Demonstrates how you can load data from an ARFF formatted file. | Class | tutorials.tools | JavaML |
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TutorialClusterEvaluation | Shows how to use the different cluster evaluation measure that are implemented in Java-ML. | Class | tutorials.clustering | JavaML |
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TutorialCrossValidation | This tutorial shows how you can do cross-validation with Java-MLAuthor:Thomas Abeel (thomas@abeel. | Class | tutorials.classification | JavaML |
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TutorialCVSameFolds | This tutorial shows how you can do multiple cross-validations with the sameAuthor:Thomas Abeel (thomas@abeel. | Class | tutorials.classification | JavaML |
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TutorialData | | Class | tutorials | JavaML |
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TutorialDataLoader | This tutorial shows how to load data from a local file. | Class | tutorials.tools | JavaML |
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TutorialDataset | This tutorial show how to create a Dataset from a collection of instances. | Class | tutorials.core | JavaML |
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TutorialDenseInstance | This tutorial shows the very first step in using Java-ML. | Class | tutorials.core | JavaML |
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TutorialEnsembleFeatureSelection | Tutorial to illustrate ensemble feature selection. | Class | tutorials.featureselection | JavaML |
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TutorialEvaluateDataset | This tutorial show how to use the EvaluateDataset class to test the performance of a classifier on a data set. | Class | tutorials.classification | JavaML |
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TutorialFeatureRanking | | Class | tutorials.featureselection | JavaML |
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TutorialFeatureScoring | | Class | tutorials.featureselection | JavaML |
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TutorialFeatureSubsetSelection | Shows the basic steps to create use a feature subset selection algorithm. | Class | tutorials.featureselection | JavaML |
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TutorialKDependentBayes | Tutorial for K Dependent Bayes classifierAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.classification | JavaML |
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TutorialKDtreeKNN | This tutorial show how to use a the k-nearest neighbors classifier. | Class | tutorials.classification | JavaML |
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TutorialKMeans | This tutorial shows how to use a clustering algorithm to cluster a data set. | Class | tutorials.clustering | JavaML |
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TutorialKNN | This tutorial show how to use a the k-nearest neighbors classifier. | Class | tutorials.classification | JavaML |
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TutorialLibSVM | This tutorial show how to use a the LibSVM classifier. | Class | tutorials.classification | JavaML |
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TutorialNaiveBayes | Tutorial for Naive Bayes classifierAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.classification | JavaML |
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TutorialRandomForest | Tutorial for the random forest classifier. | Class | tutorials.classification | JavaML |
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TutorialRecursiveMinimalEntropyPartitioning | Tutorial Recursive Minimal Entropy PartitioningAuthor:Lieven Baeyens, Thomas Abeel (thomas@abeel. | Class | tutorials.filter | JavaML |
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TutorialSampling | Sample program illustrating how to use sampling. | Class | tutorials.tools | JavaML |
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TutorialSelfOptimizingLibSVM | This tutorial show how to use a the LibSVM classifier. | Class | tutorials.classification | JavaML |
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TutorialSparseInstance | Shows how to create a SparseInstance. | Class | tutorials.core | JavaML |
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TutorialStoreData | Demonstrates how you can store data to a file. | Class | tutorials.tools | JavaML |
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TutorialWekaAttributeSelection | Tutorial how to use the Bridge to WEKA AS Evaluation , AS Search and Evaluator algorithms in Java-ML | Class | tutorials.featureselection | JavaML |
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TutorialWekaClassifier | Tutorial how to use a Weka classifier in Java-ML. | Class | tutorials.tools | JavaML |
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TutorialWekaClusterer | Tutorial how to use a Weka classifier in Java-ML. | Class | tutorials.tools | JavaML |
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TwoBinMinimalEntropyPartitioning | A filter that discretizes a range of numeric attributes in the data set into 2 nominal attributes. | Class | net.sf.javaml.filter.discretize | JavaML |
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TypeConversions | | Class | net.sf.javaml.distance.fastdtw.lang | JavaML |
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UnsetClassFilter | Filter to remove class information from a data set or instance. | Class | net.sf.javaml.filter | JavaML |
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Vectors | Static vector manipulation routines for Matlab porting and other numeric operations. | Class | net.sf.javaml.clustering.mcl | JavaML |
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WarpPath | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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WarpPathWindow | | Class | net.sf.javaml.distance.fastdtw.dtw | JavaML |
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WB | | Class | net.sf.javaml.clustering.evaluation | JavaML |
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WekaAttributeSelection | | Class | net.sf.javaml.tools.weka | JavaML |
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WekaClassifier | | Class | net.sf.javaml.tools.weka | JavaML |
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WekaClusterer | Provides a bridge between Java-ML and the clustering algorithms in WEKA. | Class | net.sf.javaml.tools.weka | JavaML |
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WekaException | This exception should be thrown when something went wrong with calls to theVersion:0. | Class | net.sf.javaml.tools.weka | JavaML |
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ZeroR | ZeroR classifier implementation. | Class | net.sf.javaml.classification | JavaML |