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jbpm-recommendation-pmml-logistic-regression from group org.kie (version 7.74.1.Final)

PMML logistic regression based prediction service

Group: org.kie Artifact: jbpm-recommendation-pmml-logistic-regression
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Download jbpm-recommendation-pmml-logistic-regression.jar (7.74.1.Final)
 

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Artifact jbpm-recommendation-pmml-logistic-regression
Group org.kie
Version 7.74.1.Final
Last update 13. July 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies kie-internal, jboss-jaxb-api_2.3_spec,
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jasima-main from group com.googlecode.jasima (version 1.3.0)

JAva SImulatior for MAnufacturing and logistics - A framework for discrete event simulation and computer experiments with a main focus on modelling and analyzing logistic/manufacturing systems.

Group: com.googlecode.jasima Artifact: jasima-main
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Download jasima-main.jar (1.3.0)
 

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Artifact jasima-main
Group com.googlecode.jasima
Version 1.3.0
Last update 28. July 2015
Organization BIBA – Bremer Institut für Produktion und Logistik GmbH
URL http://jasima.googlecode.com/
License GNU GPL version 3 or above
Dependencies amount 4
Dependencies jxl, xstream, commons-math3, jopt-simple,
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bayesianLogisticRegression from group nz.ac.waikato.cms.weka (version 1.0.5)

Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization.

Group: nz.ac.waikato.cms.weka Artifact: bayesianLogisticRegression
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Download bayesianLogisticRegression.jar (1.0.5)
 

5 downloads
Artifact bayesianLogisticRegression
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 12. April 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/bayesianLogisticRegression
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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realAdaBoost from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for boosting a 2-class classifier using the Real Adaboost method. For more information, see J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

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

1 downloads
Artifact realAdaBoost
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/realAdaBoost
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
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probabilityCalibrationTrees from group nz.ac.waikato.cms.weka (version 1.0.0)

Group: nz.ac.waikato.cms.weka Artifact: probabilityCalibrationTrees
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Download probabilityCalibrationTrees.jar (1.0.0)
 

2 downloads

supervisedAttributeScaling from group nz.ac.waikato.cms.weka (version 1.0.2)

Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .

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

1 downloads
Artifact supervisedAttributeScaling
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 30. October 2018
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/supervisedAttributeScaling
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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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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Download RBFNetwork.jar (1.0.8)
 

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

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

Group: nz.ac.waikato.cms.weka Artifact: bestFirstTree
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Download bestFirstTree.jar (1.0.4)
 

1 downloads
Artifact bestFirstTree
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 27. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/bestFirstTree
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
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kernelLogisticRegression from group nz.ac.waikato.cms.weka (version 1.0.0)

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

Group: nz.ac.waikato.cms.weka Artifact: kernelLogisticRegression
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Download kernelLogisticRegression.jar (1.0.0)
 

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Artifact kernelLogisticRegression
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 26. June 2013
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/kernelLogisticRegression
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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SPegasos from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

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

1 downloads
Artifact SPegasos
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/SPegasos
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!



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