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

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- Mahout HDFS Support

org.apache.mahout : mahout-hdfs

Scalable machine learning libraries

Последняя версия: 14.1

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- Mahout Spark Engine

org.apache.mahout : mahout-spark

Mahout Bindings for Apache Spark

Последняя версия: 14.1

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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

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Последняя версия: 14.1

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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

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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

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Mahout Examples

org.apache.mahout : mahout-examples

Scalable machine learning library examples

Последняя версия: 0.13.0

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Mahout Map-Reduce

org.apache.mahout : mahout-mr

Scalable machine learning libraries

Последняя версия: 0.13.0

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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

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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

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Последняя версия: 0.13.0

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Последняя версия: 0.13.0

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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

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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

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Последняя версия: 0.12.2

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Последняя версия: 0.12.2

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Последняя версия: 0.10.0

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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

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Mahout Utilities

org.apache.mahout : mahout-utils

Utilities for preparing content into formats for Mahout.

Последняя версия: 0.5

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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

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Mahout Taste Webapp

org.apache.mahout : mahout-taste-webapp

Mahout Taste Collaborative Filtering Web App

Последняя версия: 0.5

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Mahout Collections

org.apache.mahout : mahout-collections

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

Последняя версия: 1.0

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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

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