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Пакет: python-module-mdp
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Текущая версия: 2.5-alt1.svn20091007
Время сборки: 9 октября 2009, 10:09 ( 759.0 недели назад )
Размер архива: 19.62 Mb
Домашняя страница: http://mdp-toolkit.sourceforge.net/
Лицензия: LGPL v2
О пакете: Modular toolkit for Data Processing
Описание:
Список всех майнтейнеров, принимавших участие
в данной и/или предыдущих сборках пакета: Список rpm-пакетов, предоставляемый данным srpm-пакетом:
ACL:
Время сборки: 9 октября 2009, 10:09 ( 759.0 недели назад )
Размер архива: 19.62 Mb
Домашняя страница: http://mdp-toolkit.sourceforge.net/
Лицензия: LGPL v2
О пакете: Modular toolkit for Data Processing
Описание:
Modular toolkit for Data Processing (MDP) is a Python data processing
framework.
From the user's perspective, MDP is a collection of supervised and
unsupervised learning algorithms and other data processing units that
can be combined into data processing sequences and more complex
feed-forward network architectures.
From the scientific developer's perspective, MDP is a modular framework,
which can easily be expanded. The implementation of new algorithms is
easy and intuitive. The new implemented units are then automatically
integrated with the rest of the library.
The base of available algorithms is steadily increasing and includes, to
name but the most common, Principal Component Analysis (PCA and NIPALS),
several Independent Component Analysis algorithms (CuBICA, FastICA,
TDSEP, JADE, and XSFA), Slow Feature Analysis, Gaussian Classifiers,
Restricted Boltzmann Machine, and Locally Linear Embedding.
Текущий майнтейнер: Eugeny A. Rostovtsev (REAL) framework.
From the user's perspective, MDP is a collection of supervised and
unsupervised learning algorithms and other data processing units that
can be combined into data processing sequences and more complex
feed-forward network architectures.
From the scientific developer's perspective, MDP is a modular framework,
which can easily be expanded. The implementation of new algorithms is
easy and intuitive. The new implemented units are then automatically
integrated with the rest of the library.
The base of available algorithms is steadily increasing and includes, to
name but the most common, Principal Component Analysis (PCA and NIPALS),
several Independent Component Analysis algorithms (CuBICA, FastICA,
TDSEP, JADE, and XSFA), Slow Feature Analysis, Gaussian Classifiers,
Restricted Boltzmann Machine, and Locally Linear Embedding.
Список всех майнтейнеров, принимавших участие
в данной и/или предыдущих сборках пакета: Список rpm-пакетов, предоставляемый данным srpm-пакетом:
- python-module-binet
- python-module-mdp
- python-module-mdp-doc