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antlr3-maven-plugin from group org.antlr (version 3.5.3)

This is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

Group: org.antlr Artifact: antlr3-maven-plugin
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1 downloads
Artifact antlr3-maven-plugin
Group org.antlr
Version 3.5.3
Last update 10. April 2022
Organization not specified
URL http://antlr.org
License not specified
Dependencies amount 6
Dependencies plexus-compiler-api, plexus-build-api, antlr, slf4j-api, slf4j-simple, antlr,
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mahout from group org.apache.mahout (version 14.1)

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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Artifact mahout
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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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
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
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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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Artifact mahout-parent
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
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
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