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

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

Group: nz.ac.waikato.cms.weka Artifact: multiBoostAB
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Artifact multiBoostAB
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiBoostAB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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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1 downloads
Artifact SPegasos
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
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!

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

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

Group: nz.ac.waikato.cms.weka Artifact: tabuAndScatterSearch
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1 downloads
Artifact tabuAndScatterSearch
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/tabuAndScatterSearch
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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

Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.

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

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

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

Group: nz.ac.waikato.cms.weka Artifact: lazyBayesianRules
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Artifact lazyBayesianRules
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/lazyBayesianRules
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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

The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003; V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

Group: nz.ac.waikato.cms.weka Artifact: fuzzyLaticeReasoning
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Artifact fuzzyLaticeReasoning
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/fuzzyLaticeReasoning
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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.

Group: nz.ac.waikato.cms.weka Artifact: decorate
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1 downloads
Artifact decorate
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Newest version Yes
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!

repository4hibernate-parent from group net.sf.r4h (version 4.1.2)

The project provides an implementation of REPOSITORY PATTERN using HIBERNATE for data access. The a goal of this project is to provide an EASY TO USE API that allows to write most of CRUD operations you will need in development of end user applications in ONE LINE OF CODE even for developers who are unfamiliar with Hibernate. We provide a well tested set of CRUD operations which were assembled in more than 4 years of refactoring of projects of our clients. Instead of writing same code over and over again we encourage you to try this API on your own project and see how many lines of code YOU can replace with JUST ONE LINE.

Group: net.sf.r4h Artifact: repository4hibernate-parent
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0 downloads
Artifact repository4hibernate-parent
Group net.sf.r4h
Version 4.1.2
Last update 23. April 2012
Newest version Yes
Organization Semochkin Vitaly Evgenevich
URL http://r4h.sf.net
License GNU LESSER GENERAL PUBLIC LICENSE, Version 2.1
Dependencies amount 0
Dependencies No dependencies
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stringtemplate from group org.antlr (version 4.0.2)

StringTemplate is a java template engine for generating source code, web pages, emails, or any other formatted text output. StringTemplate is particularly good at multi-targeted code generators, multiple site skins, and internationalization/localization. It evolved over years of effort developing jGuru.com. StringTemplate also generates the stringtemplate website: http://www.stringtemplate.org and powers the ANTLR v3 code generator. Its distinguishing characteristic is that unlike other engines, it strictly enforces model-view separation. Strict separation makes websites and code generators more flexible and maintainable; it also provides an excellent defense against malicious template authors. There are currently about 600 StringTemplate source downloads a month.

Group: org.antlr Artifact: stringtemplate
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15 downloads
Artifact stringtemplate
Group org.antlr
Version 4.0.2
Last update 19. May 2011
Newest version Yes
Organization not specified
URL http://www.stringtemplate.org
License BSD licence
Dependencies amount 1
Dependencies antlr-runtime,
There are maybe transitive dependencies!

janbanery-core from group pl.project13.janbanery (version 1.2)

Kanbanery (https://kanbanery.com) is a simple but powerful Agile project management system, to be precise it focuses around the idea of Kanban, a somewhat near idea to SCRUM but with less strict rules. The heart of each Kanban flow is the Kanban board, IceBox and Archive - there are all easy accessible via this API. Janbanery wraps around the RESTful API delivered by Kanbanery while adding some more features like mass operations or filtering of results. In the end, it's very easy and pleasant to implement your own Kanbanery client be it on the desktop, mobile (android) or as for example Gradle / SBT script to take full advantage of kanbanery's features.

Group: pl.project13.janbanery Artifact: janbanery-core
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Artifact janbanery-core
Group pl.project13.janbanery
Version 1.2
Last update 09. May 2011
Newest version Yes
Organization not specified
URL http://www.blog.project13.pl
License Apache License 2.0
Dependencies amount 7
Dependencies async-http-client, gson, guava, joda-time, slf4j-api, logback-classic, logback-core,
There are maybe transitive dependencies!



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