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avatars from group net.sectorsieteg (version 1.0.0)

Group: net.sectorsieteg Artifact: avatars
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Group net.sectorsieteg
Version 1.0.0


chiSquaredAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.4)

Group: nz.ac.waikato.cms.weka Artifact: chiSquaredAttributeEval
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alternatingModelTrees from group nz.ac.waikato.cms.weka (version 1.0.0)

Grows an alternating model tree by minimising squared error. For more information see "Eibe Frank, Michael Mayo, Stefan Kramer: Alternating Model Trees. In: Proceedings of the ACM Symposium on Applied Computing, Data Mining Track, 2015".

Group: nz.ac.waikato.cms.weka Artifact: alternatingModelTrees
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Artifact alternatingModelTrees
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 18. January 2015
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/alternatingModelTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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isotonicRegression from group nz.ac.waikato.cms.weka (version 1.0.2)

Learns an isotonic regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with numeric attributes. Considers the monotonically increasing case as well as the monotonically decreasing case.

Group: nz.ac.waikato.cms.weka Artifact: isotonicRegression
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Artifact isotonicRegression
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/isotonicRegression
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
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leastMedSquared from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.

Group: nz.ac.waikato.cms.weka Artifact: leastMedSquared
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Download leastMedSquared.jar (1.0.2)
 

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Artifact leastMedSquared
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/leastMedSquared
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!

RBFNetwork from group nz.ac.waikato.cms.weka (version 1.0.8)

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

Group: nz.ac.waikato.cms.weka Artifact: RBFNetwork
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11 downloads
Artifact RBFNetwork
Group nz.ac.waikato.cms.weka
Version 1.0.8
Last update 16. January 2015
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/RBFNetwork
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

conjunctiveRule from group nz.ac.waikato.cms.weka (version 1.0.4)

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Group: nz.ac.waikato.cms.weka Artifact: conjunctiveRule
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Artifact conjunctiveRule
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/conjunctiveRule
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

multiLayerPerceptrons from group nz.ac.waikato.cms.weka (version 1.0.10)

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Group: nz.ac.waikato.cms.weka Artifact: multiLayerPerceptrons
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Download multiLayerPerceptrons.jar (1.0.10)
 

10 downloads
Artifact multiLayerPerceptrons
Group nz.ac.waikato.cms.weka
Version 1.0.10
Last update 31. October 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiLayerPerceptrons
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!



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