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consistencySubsetEval from group nz.ac.waikato.cms.weka (version 1.0.4)
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. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.
Group: nz.ac.waikato.cms.weka Artifact: consistencySubsetEval
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Artifact consistencySubsetEval
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 16. October 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/consistencySubsetEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 16. October 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/consistencySubsetEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
ssj from group ca.umontreal.iro (version 2.5)
SSJ is a Java library for stochastic simulation, developed under the direction of Pierre L'Ecuyer,
in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal.
It provides facilities for generating uniform and nonuniform random variates, computing different
measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte
Carlo methods, collecting (elementary) statistics, and programming discrete-event simulations with both
events and processes.
Artifact ssj
Group ca.umontreal.iro
Version 2.5
Last update 13. December 2012
Organization not specified
URL http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
License GPL
Dependencies amount 6
Dependencies jfreechart, mahout-collections, jcommon, colt, optimization, dsol-xml,
There are maybe transitive dependencies!
Group ca.umontreal.iro
Version 2.5
Last update 13. December 2012
Organization not specified
URL http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
License GPL
Dependencies amount 6
Dependencies jfreechart, mahout-collections, jcommon, colt, optimization, dsol-xml,
There are maybe transitive dependencies!
decorate from group nz.ac.waikato.cms.weka (version 1.0.3)
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.
1 downloads
Artifact decorate
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/decorate
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/decorate
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
org.pojava.datetime from group org.pojava (version 3.0.0)
POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates.
It parses dates from most languages and formats out of the box without having to specify which
format is expected. Defaults such as time zones, and whether to interpret an internationally
ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone
and locale and stored in a default config object that can be replaced or overridden. Multiple
languages for month names are supported without any additional configuration needed.
The net effect the default parser for a server in Paris would have a different automatic
configuration from a server in New York. Throw a random local date at either, and it'll
parse it as expected. If your server supports customers from multiple locales and time zones,
then each can be specified when parsing a date/time to resolve any ambiguities.
Artifact org.pojava.datetime
Group org.pojava
Version 3.0.0
Last update 11. March 2014
Organization not specified
URL http://www.pojava.org
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.pojava
Version 3.0.0
Last update 11. March 2014
Organization not specified
URL http://www.pojava.org
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
statistics from group de.xypron.statistics (version 1.0.9)
Xypron Statistics is a Java library which was developped with supply
chain simulation in mind. The normal, the exponential and the gamma
distribution have been included. Methods to calculate fill rate and
order rate service levels as well as safety factors are provided.
The Mersenne Twister algorithm is used to provide high quality random
number generation.
Some functions for the gamma distribution where adopted from
http://www.ssfnet.org/download/ssfnet_raceway-2.0.tar.gz.
For these the following applies:
Copyright 1999 CERN - European Organization for Nuclear Research.
Permission to use, copy, modify, distribute and sell this software and
its documentation for any purpose is hereby granted without fee,
provided that the above copyright notice appear in all copies and
that both that copyright notice and this permission notice appear in
supporting documentation. CERN makes no representations about the
suitability of this software for any purpose. It is provided "as is"
without expressed or implied warranty.
0 downloads
Artifact statistics
Group de.xypron.statistics
Version 1.0.9
Last update 22. February 2014
Organization not specified
URL http://www.xypron.de/projects/statistics/
License Apache 2
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group de.xypron.statistics
Version 1.0.9
Last update 22. February 2014
Organization not specified
URL http://www.xypron.de/projects/statistics/
License Apache 2
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
randoop from group net.sourceforge.javydreamercsw (version 1.3.2)
Randoop is an automatic unit test generator for Java. It automatically creates unit tests for your classes, in JUnit format.
Randoop generates unit tests using feedback-directed random test generation. In a nutshell, this technique randomly, but smartly, generates sequences of methods and constructor invocations for the classes under test, and uses the sequences to create tests. Randoop executes the sequences it creates, using the results of the execution to create assertions that capture the behavior or your program and that catch bugs.
Randoop has created tests that find previously unkwon errors even in widely-used libraries including Sun and IBM's JDKs. A .NET version of Randoop, used internally at Microsoft, has been used successfully by a team of test engineers to find errors in a core .NET component that has been heavily tested for years. Randoop's combination of randomized test generation and test execution results in a highly effective test generation technique.
Artifact randoop
Group net.sourceforge.javydreamercsw
Version 1.3.2
Last update 05. December 2012
Organization not specified
URL https://sourceforge.net/projects/randoopmplugin/
License MIT License
Dependencies amount 2
Dependencies manipulation, plume,
There are maybe transitive dependencies!
Group net.sourceforge.javydreamercsw
Version 1.3.2
Last update 05. December 2012
Organization not specified
URL https://sourceforge.net/projects/randoopmplugin/
License MIT License
Dependencies amount 2
Dependencies manipulation, plume,
There are maybe transitive dependencies!
jung-parent from group io.github.devlibx.jung (version 3.1)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extensible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: io.github.devlibx.jung Artifact: jung-parent
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Artifact jung-parent
Group io.github.devlibx.jung
Version 3.1
Last update 22. April 2021
Organization not specified
URL http://devlibx.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group io.github.devlibx.jung
Version 3.1
Last update 22. April 2021
Organization not specified
URL http://devlibx.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
jung-parent from group com.northdata.jung (version 2.2.0)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extendible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: com.northdata.jung Artifact: jung-parent
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0 downloads
Artifact jung-parent
Group com.northdata.jung
Version 2.2.0
Last update 18. September 2020
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group com.northdata.jung
Version 2.2.0
Last update 18. September 2020
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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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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!
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!
jung-parent from group net.sf.jung (version 2.1.1)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extendible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: net.sf.jung Artifact: jung-parent
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0 downloads
Artifact jung-parent
Group net.sf.jung
Version 2.1.1
Last update 07. September 2016
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group net.sf.jung
Version 2.1.1
Last update 07. September 2016
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
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