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.supportVector
weka.classifiers.lazy
weka.classifiers.meta
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.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.

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	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	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	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	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

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	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	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	VotedPerceptron Implementation of the voted perceptron algorithm by Freund and Schapire.

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	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	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	LogitBoost Class for performing additive logistic regression.
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	Stacking Combines several classifiers using the stacking method.
class	Vote Class for combining classifiers.

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	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	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.

Uses of TechnicalInformationHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement TechnicalInformationHandler

class	J48 Class for generating a pruned or unpruned C4.5 decision tree.
class	LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P M5Base.
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).

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	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	FarthestFirst Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	SimpleKMeans Cluster data using the k means algorithm.

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	MinkowskiDistance Implementing Minkowski 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	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.

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.

Uses of TechnicalInformationHandler in weka.gui.boundaryvisualizer

Classes in weka.gui.boundaryvisualizer that implement TechnicalInformationHandler

class

BoundaryVisualizer BoundaryVisualizer.