JarCasting
JarCasting JarCasting

Группа библиотек: org.apache.mahout

Mahout Core

 org.apache.mahout : mahout-core

High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Последняя версия: 14.1

Дата:

- Mahout HDFS Support

 org.apache.mahout : mahout-hdfs

Scalable machine learning libraries

Последняя версия: 14.1

Дата:

- Mahout Spark Engine

 org.apache.mahout : mahout-spark

Mahout Bindings for Apache Spark

Последняя версия: 14.1

Дата:

Apache Mahout

 org.apache.mahout : mahout

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.

Последняя версия: 14.1

Дата:

Mahout Engine

 org.apache.mahout : mahout-engine

Parent of Various Engines on which you can run Mahout.

Последняя версия: 14.1

Дата:

Mahout Distribution

 org.apache.mahout : apache-mahout-distribution

Distribution Package

Последняя версия: 14.1

Дата:

Mahout Math

 org.apache.mahout : mahout-math

High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Последняя версия: 0.13.0

Дата:

Mahout Math Scala bindings

 org.apache.mahout : mahout-math-scala_2.10

High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Последняя версия: 0.13.0

Дата:

Mahout Examples

 org.apache.mahout : mahout-examples

Scalable machine learning library examples

Последняя версия: 0.13.0

Дата:

Mahout Map-Reduce

 org.apache.mahout : mahout-mr

Scalable machine learning libraries

Последняя версия: 0.13.0

Дата:

Mahout Build Tools

 org.apache.mahout : mahout-buildtools

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field. We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

Последняя версия: 0.13.0

Дата:

Mahout Integration

 org.apache.mahout : mahout-integration

Optional components of Mahout which generally support interaction with third party systems, formats, APIs, etc.

Последняя версия: 0.13.0

Дата:

Mahout Spark bindings

 org.apache.mahout : mahout-spark_2.10

Mahout Bindings for Apache Spark

Последняя версия: 0.13.0

Дата:

Mahout H2O backend

 org.apache.mahout : mahout-h2o_2.10

H2O Backend for Mahout DSL

Последняя версия: 0.13.0

Дата:

Mahout Native VienniaCL OpenCL Bindings

 org.apache.mahout : mahout-native-viennacl_2.10

Native Structures and interfaces to be used from Mahout math-scala.

Последняя версия: 0.13.0

Дата:

Mahout Native VienniaCL OpenMP Bindings

 org.apache.mahout : mahout-native-viennacl-omp_2.10

Native Structures and interfaces to be used from Mahout math-scala.

Последняя версия: 0.13.0

Дата:

Mahout Flink bindings

 org.apache.mahout : mahout-flink_2.10

Mahout Bindings for Apache Flink

Последняя версия: 0.12.2

Дата:

Mahout Spark bindings shell

 org.apache.mahout : mahout-spark-shell_2.10

Mahout Bindings for Apache Spark

Последняя версия: 0.12.2

Дата:

Mahout Release Package

 org.apache.mahout : mahout-distribution

Distribution Package

Последняя версия: 0.10.0

Дата:

Mahout Math/Scala wrappers

 org.apache.mahout : mahout-math-scala

High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Последняя версия: 0.9

Дата:

Mahout Utilities

 org.apache.mahout : mahout-utils

Utilities for preparing content into formats for Mahout.

Последняя версия: 0.5

Дата:

Mahout Eclipse

 org.apache.mahout : mahout-eclipse-support

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.

Последняя версия: 0.5

Дата:

Mahout Taste Webapp

 org.apache.mahout : mahout-taste-webapp

Mahout Taste Collaborative Filtering Web App

Последняя версия: 0.5

Дата:

Mahout Collections

 org.apache.mahout : mahout-collections

Primitive-type collections based on CERN's Colt Java API

Последняя версия: 1.0

Дата:

Maven Mojo to generate code for collections

 org.apache.mahout : mahout-collection-codegen-plugin

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field. We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

Последняя версия: 1.0

Дата:

Mahout Common Maven Parent

 org.apache.mahout : mahout-parent

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.

Последняя версия: 0.3

Дата:

  • 1