All Downloads are FREE. Search and download functionalities are using the official Maven repository.

Download .test JAR files with dependency

Search JAR files by class name

richfaces-core-impl from group org.richfaces.core (version 4.3.7.Final)

The RichFaces Core Implementation.

Group: org.richfaces.core Artifact: richfaces-core-impl
Show all versions Show documentation Show source 
 

50 downloads
Artifact richfaces-core-impl
Group org.richfaces.core
Version 4.3.7.Final
Last update 23. May 2014
Newest version Yes
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies richfaces-core-api, cssparser, guava,
There are maybe transitive dependencies!

notbed-util from group com.github.napp-com (version 1.1.8)

Util

Group: com.github.napp-com Artifact: notbed-util
Show all versions Show documentation Show source 
 

0 downloads
Artifact notbed-util
Group com.github.napp-com
Version 1.1.8
Last update 07. May 2014
Newest version Yes
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies commons-logging,
There are maybe transitive dependencies!

killbill-overdue from group com.ning.billing (version 0.8.14)

Group: com.ning.billing Artifact: killbill-overdue
Show all versions Show documentation Show source 
 

1 downloads
Artifact killbill-overdue
Group com.ning.billing
Version 0.8.14
Last update 04. May 2014
Newest version Yes
Organization not specified
URL Not specified
License not specified
Dependencies amount 9
Dependencies guava, killbill-api, killbill-internal-api, killbill-util, killbill-clock, killbill-queue, joda-time, jdbi, config-magic,
There are maybe transitive dependencies!

killbill-osgi from group org.kill-bill.billing (version 0.11.3)

Group: org.kill-bill.billing Artifact: killbill-osgi
Show all versions Show documentation Show source 
 

0 downloads
Artifact killbill-osgi
Group org.kill-bill.billing
Version 0.11.3
Last update 01. May 2014
Newest version Yes
Organization not specified
URL Not specified
License not specified
Dependencies amount 16
Dependencies jsr305, guava, bonecp, c3p0, javax.inject, javax.servlet-api, org.apache.felix.framework, jdbi, killbill-api, killbill-internal-api, killbill-osgi-bundles-lib-killbill, killbill-util, killbill-queue, org.osgi.compendium, config-magic, slf4j-api,
There are maybe transitive dependencies!

DTNB from group nz.ac.waikato.cms.weka (version 1.0.3)

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.

Group: nz.ac.waikato.cms.weka Artifact: DTNB
Show all versions Show documentation Show source 
 

0 downloads
Artifact DTNB
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 30. April 2014
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/DTNB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

denormalize from group nz.ac.waikato.cms.weka (version 1.0.3)

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

Group: nz.ac.waikato.cms.weka Artifact: denormalize
Show all versions Show documentation Show source 
 

0 downloads
Artifact denormalize
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/denormalize
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

dagging from group nz.ac.waikato.cms.weka (version 1.0.3)

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. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

Group: nz.ac.waikato.cms.weka Artifact: dagging
Show all versions Show documentation Show source 
 

2 downloads
Artifact dagging
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/dagging
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

costSensitiveAttributeSelection from group nz.ac.waikato.cms.weka (version 1.0.3)

This package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.

Group: nz.ac.waikato.cms.weka Artifact: costSensitiveAttributeSelection
Show all versions Show documentation Show source 
 

0 downloads
Artifact costSensitiveAttributeSelection
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/costSensitiveAttributeSelection
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
Show all versions Show documentation Show source 
 

0 downloads
Artifact conjunctiveRule
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 29. April 2014
Newest version Yes
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!

complementNaiveBayes from group nz.ac.waikato.cms.weka (version 1.0.3)

Class for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.

Group: nz.ac.waikato.cms.weka Artifact: complementNaiveBayes
Show all versions Show documentation Show source 
 

0 downloads
Artifact complementNaiveBayes
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/complementNaiveBayes
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!



Page 1293 from 1395 (items total 13942)

Our partner network: Download PHP libraries without composer, Online Shopping in Tbilisi (Georgia)


© 2018 Weber Informatics LLC