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church.i18n.error from group church.i18n (version 2019.0)

Group: church.i18n Artifact: church.i18n.error
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jcobyla from group de.xypron.jcobyla (version 1.3)

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

Group: de.xypron.jcobyla Artifact: jcobyla
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3 downloads
Artifact jcobyla
Group de.xypron.jcobyla
Version 1.3
Last update 12. January 2019
Newest version Yes
Organization not specified
URL https://github.com/xypron/jcobyla
License The MIT License
Dependencies amount 0
Dependencies No dependencies
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quality-check from group net.sf.qualitycheck (version 1.3)

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

Group: net.sf.qualitycheck Artifact: quality-check
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3 downloads
Artifact quality-check
Group net.sf.qualitycheck
Version 1.3
Last update 01. August 2013
Newest version Yes
Organization not specified
URL http://qualitycheck.sourceforge.net/modules/quality-check/
License The Apache Software License, Version 2.0
Dependencies amount 5
Dependencies jsr305, commons-logging, junit, slf4j-api, slf4j-simple,
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quality-parent from group net.sf.qualitycheck (version 1.3)

Group: net.sf.qualitycheck Artifact: quality-parent
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toniclf from group net.sf.squirrel-sql.thirdparty-non-maven (version 1.0.5)

This is the tonic look-and-feel packaged to be distributed with the SQuirreLSQL client. This pluggable look and feel is a free substitute for the default native look and feel of Swing, 'Metal', distributed under the GNU Lesser General Public License. Metal lacks both in usability and aesthetics. It contains considerable graphical noise, distracting the user from the key elements of the GUI. Tonic, on the other hand, tries to provide a clean, balanced look and an improved feel. Tonic is available free of charge both for commercial and non-commercial applications. It lends a professional touch and a very tidy and clean interface to your Swing based applications.

Group: net.sf.squirrel-sql.thirdparty-non-maven Artifact: toniclf
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3 downloads
Artifact toniclf
Group net.sf.squirrel-sql.thirdparty-non-maven
Version 1.0.5
Last update 03. October 2009
Newest version Yes
Organization not specified
URL http://www.digitprop.com/tonic/tonic.php
License GNU Lesser General Public License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!

mahout from group org.apache.mahout (version 0.13.0)

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Group: org.apache.mahout Artifact: mahout
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0 downloads
Artifact mahout
Group org.apache.mahout
Version 0.13.0
Last update 15. April 2017
Newest version Yes
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!

mahout-eclipse-support from group org.apache.mahout (version 0.5)

Group: org.apache.mahout Artifact: mahout-eclipse-support
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1 downloads
Artifact mahout-eclipse-support
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Newest version Yes
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!

mahout-parent from group org.apache.mahout (version 0.3)

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Group: org.apache.mahout Artifact: mahout-parent
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0 downloads
Artifact mahout-parent
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Newest version Yes
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!



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