Debian Science Project
Summary
Machine learning
Debian Science Machine Learning packages

This metapackage will install packages useful for machine learning. Included packages range from knowledge-based (expert) inference systems to software implementing the advanced statistical methods that currently dominate the field.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

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Debian Science Machine learning packages

Official Debian packages with high relevance

Gprolog
GNU Prolog compiler
Maintainer: Salvador Abreu
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GNU Prolog is a free Prolog compiler with constraint solving over finite domains (FD). GNU Prolog is largely compliant with the ISO standard and is part of the Prolog Commons initiative.

This package contains the compiler and runtime system for the ISO standard version of GNU Prolog, including the prototype modules implementation.

Libcomplearn-dev
machine-learning through data compression development files
Maintainer: Rudi Cilibrasi
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complearn is a library for parameter-free universal learning. Using this library, developers can access a wealth of powerful and general techniques in artificial intelligence and pattern recognition including fields such as genomics, language evolution, music recognition, and much more

Libfann-dev
vývojové knižnice a hlavičkové súbory pre FANN
Maintainer: Christian Kastner
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Fast Artificial Neural Network Library je slobodná open source knižnica neurónových sietí, ktorá implementuje viacvrstvové umelé neurónové siete v jazyku C s podporou plne prepojených aj riedko prepojených sietí. Podporuje beh na viacerých platformách s pevnou aj pohyblivou desatinnou čiarkou. Obsahuje platformu na jednoduchú prácu s tréningovými sadami dát. Jednoducho sa používa, je všestranne použiteľná, dobre zdokumentovaná a rýchla.

Tento balík obsahuje hlavičkové súbory a statické knižnice potrebné na vývoj aplikácií používajúcich libfann.

Liblinear-dev
vývojové knižnice a hlavičkové súbory pre LIBLINEAR
Maintainer: Christian Kastner
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LIBLINEAR je knižnica na výuku lineárnych klasifikátorov pre rozsiahle aplikácie. Podporuje SVM (Support Vector Machines) so stratou L2 a L1, logistickú regresiu, viactriednu klasifikáciu a tiež Linear Programming Machines (L1-regularizované SVM). Jeho výpočtová zložitosť rastie lineárne v závislosti od počtu vzorových príkladov, vďaka čomu je jedným z najrýchlejších existujúcich riešiteľov SVM. Tiež poskytuje väzby pre jazyk Python.

Tento balík obsahuje hlavičkové súbory a statické knižnice.

Libocas-dev
Development libraries and header files for LIBOCAS
Maintainer: Christian Kastner
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This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs.

This package contains the header files and static libraries.

Libqsearch-dev
nondeterministic quartet tree search library for unrooted trees
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qsearch is a library for universal hierarchical clustering using an arbitrary distance matrix as input. It searches through the space of all possible unrooted trees of a given size and finds the closest match based on a weighted quartet cost function determined by the distance matrix. When used in combination with other feature extraction libraries such as libcomplearn this system can be used for fast and accurate phylogenetic reconstruction, linguistic analysis, or stemmatology.

Libshogun-dev
Large Scale Machine Learning Toolbox
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SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.

SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.

Libsvm-dev
LIBSVM header files
Maintainer: Chen-Tse Tsai (Adrian Bunk)
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LIBSVM, a machine-learning library, is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection.

This package contains the development header files.

Libtorch3-dev
State of the art machine learning library - development files
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Torch is a machine-learning library, written in C++. Its aim is to provide the state-of-the-art of the best algorithms.

  • Many gradient-based methods, including multi-layered perceptrons, radial basis functions, and mixtures of experts. Many small "modules" (Linear module, Tanh module, SoftMax module, ...) can be plugged together.
  • Support Vector Machine, for classification and regression.
  • Distribution package, includes Kmeans, Gaussian Mixture Models, Hidden Markov Models, and Bayes Classifier, and classes for speech recognition with embedded training.
  • Ensemble models such as Bagging and Adaboost.
  • Non-parametric models such as K-nearest-neighbors, Parzen Regression and Parzen Density Estimator.

This package is the Torch development package (header files and static library.)

Python3-mdp
Modular toolkit for Data Processing
Maintainer: Tiziano Zito
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Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.

This package contains MDP for Python 3.

The package is enhanced by the following packages: python3-sklearn
Python3-sklearn
Python modules for machine learning and data mining - Python 3
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scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality:

  • Gaussian Mixture Models
  • Manifold learning
  • kNN
  • SVM (via LIBSVM)

This package contains the Python 3 version.

The package is enhanced by the following packages: python3-sklearn-pandas
Yap
High-performance Prolog System
Maintainer: Ralf Treinen
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High-performance Prolog compiler developed at LIACC/Universidade do Porto and at COPPE Sistemas/UFRJ. The YAP Prolog engine is based in the Warren Abstract Machine, with several optimizations for better performance. YAP follows the Edinburgh tradition, and is largely compatible with the ISO-Prolog standard and with Quintus and SICStus Prolog.

YAP features a constraint solver over real numbers, and support for constraint handling rules (CHR).

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