Uses of Interface
weka.core.TechnicalInformationHandler

Packages that use TechnicalInformationHandler

weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.search.local
weka.classifiers.functions
weka.classifiers.functions.pace
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.neighboursearch.kdtrees
weka.core.stemmers
weka.datagenerators.classifiers.classification
weka.datagenerators.clusterers
weka.experiment
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.gui.boundaryvisualizer

Uses of TechnicalInformationHandler in weka.associations

Classes in weka.associations that implement TechnicalInformationHandler

class	Apriori Class implementing an Apriori-type algorithm.
class	FPGrowth Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
class	GeneralizedSequentialPatterns Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
class	PredictiveApriori Class implementing the predictive apriori algorithm to mine association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence.
class	Tertius Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P.

Uses of TechnicalInformationHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement TechnicalInformationHandler

class	CfsSubsetEval CfsSubsetEval : Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred. For more information see: M.
class	ConsistencySubsetEval ConsistencySubsetEval : Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
class	GeneticSearch GeneticSearch: Performs a search using the simple genetic algorithm described in Goldberg (1989). For more information see: David E.
class	LinearForwardSelection LinearForwardSelection: Extension of BestFirst.
class	RaceSearch Races the cross validation error of competing attribute subsets.
class	RandomSearch RandomSearch : Performs a Random search in the space of attribute subsets.
class	RankSearch RankSearch : Uses an attribute/subset evaluator to rank all attributes.
class	ReliefFAttributeEval ReliefFAttributeEval : Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
class	ScatterSearchV1 Class for performing the Sequential Scatter Search.
class	SVMAttributeEval SVMAttributeEval : Evaluates the worth of an attribute by using an SVM classifier.
class	WrapperSubsetEval WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.

Uses of TechnicalInformationHandler in weka.classifiers

Classes in weka.classifiers that implement TechnicalInformationHandler

class	BVDecompose Class for performing a Bias-Variance decomposition on any classifier using the method specified in: Ron Kohavi, David H.
class	BVDecomposeSegCVSub This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1). The Kohavi and Wolpert definition of bias and variance is specified in (2). The Webb definition of bias and variance is specified in (3). Geoffrey I.

Uses of TechnicalInformationHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement TechnicalInformationHandler

class	AODE AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
class	AODEsr AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value. For more information, see: Fei Zheng, Geoffrey I.
class	BayesianLogisticRegression Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier. For more information see, Jason D.
class	DMNBtext Class for building and using a Discriminative Multinomial Naive Bayes classifier.
class	HNB Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC. For more information refer to: H.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesMultinomialUpdateable Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesSimple Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution. For more information, see Richard Duda, Peter Hart (1973).
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.
class	WAODE WAODE contructs the model called Weightily Averaged One-Dependence Estimators. For more information, see L.

Uses of TechnicalInformationHandler in weka.classifiers.bayes.net

Classes in weka.classifiers.bayes.net that implement TechnicalInformationHandler

class	ADNode The ADNode class implements the ADTree datastructure which increases the speed with which sub-contingency tables can be constructed from a data set in an Instances object.
class	BIFReader Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format. For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).

Uses of TechnicalInformationHandler in weka.classifiers.bayes.net.search.local

Classes in weka.classifiers.bayes.net.search.local that implement TechnicalInformationHandler

class	K2 This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables. For more information see: G.F.
class	SimulatedAnnealing This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure. For more information see: R.R.
class	TabuSearch This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
class	TAN This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree. For more information see: N.

Uses of TechnicalInformationHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement TechnicalInformationHandler

class	GaussianProcesses Implements Gaussian Processes for regression without hyperparameter-tuning.
class	LeastMedSq Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
class	LibLINEAR A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
class	LibSVM A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
class	Logistic Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
class	PaceRegression Class for building pace regression linear models and using them for prediction.
class	SimpleLogistic Classifier for building linear logistic regression models.
class	SMO Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	SMOreg SMOreg implements the support vector machine for regression.
class	SPegasos Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
class	VotedPerceptron Implementation of the voted perceptron algorithm by Freund and Schapire.
class	Winnow Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N.

Uses of TechnicalInformationHandler in weka.classifiers.functions.pace

Classes in weka.classifiers.functions.pace that implement TechnicalInformationHandler

class	ChisqMixture Class for manipulating chi-square mixture distributions.
class	MixtureDistribution Abtract class for manipulating mixture distributions.
class	NormalMixture Class for manipulating normal mixture distributions.

Uses of TechnicalInformationHandler in weka.classifiers.functions.supportVector

Classes in weka.classifiers.functions.supportVector that implement TechnicalInformationHandler

class	Puk The Pearson VII function-based universal kernel. For more information see: B.
class	RegSMO Implementation of SMO for support vector regression as described in : A.J.
class	RegSMOImproved Learn SVM for regression using SMO with Shevade, Keerthi, et al.
class	StringKernel Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2]. For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.

Uses of TechnicalInformationHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement TechnicalInformationHandler

class	IB1 Nearest-neighbour classifier.
class	IBk K-nearest neighbours classifier.
class	KStar K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
class	LBR Lazy Bayesian Rules Classifier.
class	LWL Locally weighted learning.

Uses of TechnicalInformationHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement TechnicalInformationHandler

class	AdaBoostM1 Class for boosting a nominal class classifier using the Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	Bagging Class for bagging a classifier to reduce variance.
class	ClassificationViaRegression Class for doing classification using regression methods.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
class	Dagging This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	Grading Implements Grading.
class	LogitBoost Class for performing additive logistic regression.
class	MetaCost This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
class	OrdinalClassClassifier Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
class	RandomSubSpace This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
class	RotationForest Class for construction a Rotation Forest.
class	Stacking Combines several classifiers using the stacking method.
class	StackingC Implements StackingC (more efficient version of stacking). For more information, see A.K.
class	Vote Class for combining classifiers.

Uses of TechnicalInformationHandler in weka.classifiers.meta.nestedDichotomies

Classes in weka.classifiers.meta.nestedDichotomies that implement TechnicalInformationHandler

class	ClassBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	DataNearBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	ND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.

Uses of TechnicalInformationHandler in weka.classifiers.mi

Classes in weka.classifiers.mi that implement TechnicalInformationHandler

class	CitationKNN Modified version of the Citation kNN multi instance classifier. For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
class	MDD Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998).
class	MIBoost MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. For more information about Adaboost, see: Yoav Freund, Robert E.
class	MIDD Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998).
class	MIEMDD EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
class	MINND Multiple-Instance Nearest Neighbour with Distribution learner. It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
class	MIOptimalBall This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
class	MISMO Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	MISVM Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
class	MIWrapper A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E.

Uses of TechnicalInformationHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement TechnicalInformationHandler

class

VFI Classification by voting feature intervals.

Uses of TechnicalInformationHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement TechnicalInformationHandler

class	DecisionTable Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables.
class	DTNB Class for building and using a decision table/naive bayes hybrid classifier.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
class	M5Rules Generates a decision list for regression problems using separate-and-conquer.
class	NNge Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
class	OneR Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
class	PART Class for generating a PART decision list.
class	Prism Class for building and using a PRISM rule set for classification.

Uses of TechnicalInformationHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement TechnicalInformationHandler

class	ADTree Class for generating an alternating decision tree.
class	BFTree Class for building a best-first decision tree classifier.
class	FT Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
class	Id3 Class for constructing an unpruned decision tree based on the ID3 algorithm.
class	J48 Class for generating a pruned or unpruned C4.5 decision tree.
class	J48graft Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
class	LADTree Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
class	LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P M5Base.
class	NBTree Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).
class	SimpleCart Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H.
class	UserClassifier Interactively classify through visual means.

Uses of TechnicalInformationHandler in weka.classifiers.trees.m5

Classes in weka.classifiers.trees.m5 that implement TechnicalInformationHandler

class

M5Base M5Base.

Uses of TechnicalInformationHandler in weka.clusterers

Classes in weka.clusterers that implement TechnicalInformationHandler

class	CLOPE Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
class	Cobweb Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	DBScan Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
class	FarthestFirst Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	OPTICS Mihael Ankerst, Markus M.
class	sIB Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
class	XMeans Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.

Uses of TechnicalInformationHandler in weka.core

Classes in weka.core that implement TechnicalInformationHandler

class	ChebyshevDistance Implements the Chebyshev distance.
class	EuclideanDistance Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	ManhattanDistance Implements the Manhattan distance (or Taxicab geometry).
class	Optimization Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.

Uses of TechnicalInformationHandler in weka.core.neighboursearch

Classes in weka.core.neighboursearch that implement TechnicalInformationHandler

class	BallTree Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	CoverTree Class implementing the CoverTree datastructure. The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
class	KDTree Class implementing the KDTree search algorithm for nearest neighbour search. The connection to dataset is only a reference.

Uses of TechnicalInformationHandler in weka.core.neighboursearch.balltrees

Classes in weka.core.neighboursearch.balltrees that implement TechnicalInformationHandler

class	BottomUpConstructor The class that constructs a ball tree bottom up.
class	MedianDistanceFromArbitraryPoint Class that splits a BallNode of a ball tree using Uhlmann's described method. For information see: Jeffrey K.
class	MiddleOutConstructor The class that builds a BallTree middle out. For more information see also: Andrew W.
class	PointsClosestToFurthestChildren Implements the Moore's method to split a node of a ball tree. For more information please see section 2 of the 1st and 3.2.3 of the 2nd: Andrew W.
class	TopDownConstructor The class implementing the TopDown construction method of ball trees.

Uses of TechnicalInformationHandler in weka.core.neighboursearch.kdtrees

Classes in weka.core.neighboursearch.kdtrees that implement TechnicalInformationHandler

class	KMeansInpiredMethod The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum. For more information see also: Ashraf Masood Kibriya (2007).
class	MedianOfWidestDimension The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread. For more information see also: Jerome H.
class	MidPointOfWidestDimension The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread. For more information see also: Andrew Moore (1991).
class	SlidingMidPointOfWidestSide The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.

Uses of TechnicalInformationHandler in weka.core.stemmers

Classes in weka.core.stemmers that implement TechnicalInformationHandler

class	IteratedLovinsStemmer An iterated version of the Lovins stemmer.
class	LovinsStemmer A stemmer based on the Lovins stemmer, described here: Julie Beth Lovins (1968).

Uses of TechnicalInformationHandler in weka.datagenerators.classifiers.classification

Classes in weka.datagenerators.classifiers.classification that implement TechnicalInformationHandler

class	Agrawal Generates a people database and is based on the paper by Agrawal et al.: R.
class	LED24 This generator produces data for a display with 7 LEDs.

Uses of TechnicalInformationHandler in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement TechnicalInformationHandler

class

BIRCHCluster Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center.

Uses of TechnicalInformationHandler in weka.experiment

Classes in weka.experiment that implement TechnicalInformationHandler

class

PairedCorrectedTTester Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic. For more information see: Claude Nadeau, Yoshua Bengio (2001).

Uses of TechnicalInformationHandler in weka.filters.supervised.attribute

Classes in weka.filters.supervised.attribute that implement TechnicalInformationHandler

class	Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	NominalToBinary Converts all nominal attributes into binary numeric attributes.
class	PLSFilter Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

Uses of TechnicalInformationHandler in weka.filters.supervised.instance

Classes in weka.filters.supervised.instance that implement TechnicalInformationHandler

class

SMOTE Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).

Uses of TechnicalInformationHandler in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement TechnicalInformationHandler

class	KernelFilter Converts the given set of predictor variables into a kernel matrix.
class	PKIDiscretize Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values. For more information, see: Ying Yang, Geoffrey I.
class	RandomProjection Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e.
class	RELAGGS A propositionalization filter inspired by the RELAGGS algorithm. It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
class	Wavelet A filter for wavelet transformation. For more information see: Wikipedia (2004).

Uses of TechnicalInformationHandler in weka.gui.boundaryvisualizer

Classes in weka.gui.boundaryvisualizer that implement TechnicalInformationHandler

class

BoundaryVisualizer BoundaryVisualizer.