Debian Science Project
Summary
Machine learning
pacchetti di apprendimento automatico per Debian Science

Questo metapacchetto installa i pacchetti utili all'apprendimento automatico. I pacchetti inclusi vanno dai sistemi di inferenza (esperti) basati sulla conoscenza fino al software che implementa i metodi statistici avanzati che attualmente dominano il campo.

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

Links to other tasks

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
file header e librerie di sviluppo per FANN
Maintainer: Christian Kastner
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La libreria FANN (Fast Artificial Neural Network, veloce rete neurale artificiale) è una libreria per rete neurale libera e open source che implementa reti neurali artificiali multilivello in C con la gestione sia di reti completamente connesse sia di reti connesse in modo sparso. Sono gestite le esecuzioni multipiattaforma in virgola fissa e in virgola mobile. Include un'infrastruttura per gestire in modo semplice insiemi di dati di addestramento. È facile da usare, versatile, ben documentata e veloce.

Questo pacchetto contiene i file header e le librerie statiche che sono necessari per sviluppare applicazioni per libfann.

Liblinear-dev
file header e librerie di sviluppo per LIBLINEAR
Maintainer: Christian Kastner
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LIBLINEAR è una libreria per classificatori lineari per apprendimento per applicazioni su larga scala. Gestisce SVM (Support Vector Machines, macchine a vettori di supporto) con perdita L2 e L1, regressioni logistiche, classificazioni multi-classe e anche macchine di programmazione lineare (SVM con regolarizzazione L1). La sua complessità computazionale scala linearmente con il numero di esempi di addestramento, e ciò la rende uno dei più veloci risolutori di SVM in circolazione.

Questo pacchetto contiene i file header e le librerie statiche.

Libocas-dev
file header e librerie di sviluppo per LIBOCAS
Maintainer: Christian Kastner
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Questa libreria implementa OCAS (Optimized Cutting Plane Algorithm) per addestrare classificatori SVM (Support Vector Machine) lineari a partire da dati su larga scala. Il lavoro di calcolo di OCAS scala in modo lineare con il numero di esempi di addestramento. È uno dei risolutori SVM più veloci disponibili per la risoluzione di SVM L2 regolarizzate multiclasse e lineari.

Questo pacchetto contiene i file header e le librerie statiche.

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
strumenti per l'apprendimento automatico su larga scala
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SHOGUN è un insieme di strumenti per l'apprendimento automatico mirato su algoritmi di tipo "large scale kernel method", in particolare su Support Vector Machine (SVM), con un orientamento alla bioinformatica. Fornisce un oggetto SVM generico che funge da interfaccia per molte diverse implementazioni SVM. Ciascuna SVM può essere combinata con una delle molte varietà di kernel implementate. Può gestire combinazioni lineari pesate di più sub-kernel (non necessariamente tutti operanti sullo stesso dominio) in cui la distribuzione ottimale dei pesi dei singoli sub-kernel può essere appresa tramite Multiple Kernel Learning. Oltre a problemi di regressione e categorizzazione in 2 classi, gestisce un gran numero di metodi lineari come LDA (Linear Discriminant Analysis), LPM (Linear Programming Machine), perceptron (kernel) e algoritmi per addestrare modelli di Markov nascosti. Gli oggetti-caratteristiche di input possono essere densi, sparsi o stringhe, possono essere di qualsiasi tipo tra int, short, double, char, e possono essere convertiti in tipi diversi. A ciascun oggetto-caratteristica si possono attaccare catene di preprocessori (ad esempio uno che sottrae la media) per permettere una pre-elaborazione "al volo".

SHOGUN è disponibile in diverse varianti, una versione a sé stante e anche con interfacce specifiche per Matlab™, R, Octave, Readline e Python. Questo pacchetto contiene i file per lo sviluppo necessari per creare eseguibili a sé stanti.

Libsvm-dev
file header per LIBSVM
Maintainer: Chen-Tse Tsai (Adrian Bunk)
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LIBSVM, una libreria per apprendimento macchina, è un pacchetto facile da usare per gestire regressione e classificazione di vettori, e SVM a una classe. Gestisce la classificazione multiclasse, output delle probabilità e selezione dei parametri.

Questo pacchetto contiene i file header di sviluppo.

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
toolkit modulare per elaborazione dei dati
Maintainer: Tiziano Zito
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Infrastruttura Python per elaborazione di dati per creare software per complesse elaborazioni di dati combinando in pipe e reti algoritmi largamente usati di apprendimento macchina. Gli algoritmi implementati includono: PCA (Principal Component Analysis), ICA (Independent Component Analysis), SFA (Slow Feature Analysis), ISFA (Independent Slow Feature Analysis), GNG (Growing Neural Gas), analisi fattoriale, FDA (Fisher Discriminant Analysis) e classificatori gaussiani.

Questo pacchetto contiene MDP per Python 3.

The package is enhanced by the following packages: python3-sklearn
Python3-sklearn
moduli Python per l'apprendimento automatico e il data mining - Python 3
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scikit-learn è una raccolta di moduli Python relativi ad apprendimento automatico/statistico e data mining. Una lista non esaustiva di funzionalità incluse:

  • modelli misti gaussiani,
  • apprendimento tramite varietà,
  • kNN,
  • SVM (tramite LIBSVM).

Questo pacchetto contiene la versione per Python 3.

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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