Machine Learning Open Source Software
To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing. Submission instructions are availablehere.
A Library for Locally Weighted Projection Regression
Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; 9(Apr):623--626, 2008.
Shark
Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; 9(Jun):993--996, 2008.
LIBLINEAR: A Library for Large Linear Classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; 9(Aug):1871--1874, 2008.
JNCC2: The Java Implementation Of Naive Credal Classifier 2
Giorgio Corani, Marco Zaffalon; 9(Dec):2695--2698, 2008.
Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
Abhik Shah, Peter Woolf; 10(Feb):159--162, 2009.
Nieme: Large-Scale Energy-Based Models
Francis Maes; 10(Mar):743--746, 2009.
Java-ML: A Machine Learning Library
Thomas Abeel, Yves Van de Peer, Yvan Saeys; 10(Apr):931--934, 2009.
Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
Troy Raeder, Nitesh V. Chawla; 10(Jul):1387--1390, 2009.
Dlib-ml: A Machine Learning Toolkit
Davis E. King; 10(Jul):1755--1758, 2009.
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
Brian Tanner, Adam White; 10(Sep):2133--2136, 2009.
DL-Learner: Learning Concepts in Description Logics
Jens Lehmann; 10(Nov):2639−2642, 2009.
Error-Correcting Output Codes Library
Sergio Escalera, Oriol Pujol, Petia Radeva; 11(Feb):661−664, 2010.
PyBrain
Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber; 11(Feb):743−746, 2010.
Continuous Time Bayesian Network Reasoning and Learning Engine
Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; 11(Mar):1137−1140, 2010.
SFO: A Toolbox for Submodular Function Optimization
Andreas Krause; 11(Mar):1141−1144, 2010.
MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; 11(May):1601−1604, 2010.
FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; 11(May):1733−1736, 2010.
The SHOGUN Machine Learning Toolbox
Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojtěch Franc; 11(Jun):1799−1802, 2010.
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; 11(Jul):2051−2055, 2010.
Model-based Boosting 2.0
Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; 11(Aug):2109−2113, 2010.
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris M. Mooij; 11(Aug):2169−2173, 2010.
Gaussian Processes for Machine Learning (GPML) Toolbox
Carl Edward Rasmussen, Hannes Nickisch; 11(Nov):3011−3015, 2010.
CARP: Software for Fishing Out Good Clustering Algorithms
Volodymyr Melnykov, Ranjan Maitra; 12(Jan):69−73, 2011.
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; 12(Jun):2021−2025, 2011.
MSVMpack: A Multi-Class Support Vector Machine Package
Fabien Lauer, Yann Guermeur; 12(Jul):2293−2296, 2011.
Waffles: A Machine Learning Toolkit
Michael Gashler; 12(Jul):2383−2387, 2011.
MULAN: A Java Library for Multi-Label Learning
Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; 12(Jul):2411−2414, 2011.
LPmade: Link Prediction Made Easy
Ryan N. Lichtenwalter, Nitesh V. Chawla; 12(Aug):2489−2492, 2011.
Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay; 12(Oct):2825−2830, 2011.
The Stationary Subspace Analysis Toolbox
Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller; 12(Oct):3065−3069, 2011.
MULTIBOOST: A Multi-purpose Boosting Package
Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; 13(Mar):549−553, 2012.
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
Stephen R. Piccolo, Lewis J. Frey; 13(Mar):555−559, 2012.
[abs][pdf] [code][sourceforge.net]
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
Chiwoo Park, Jianhua Z. Huang, Yu Ding; 13(Mar):775−779, 2012.
NIMFA : A Python Library for Nonnegative Matrix Factorization
Marinka Žitnik, Blaž Zupan; 13(Mar):849−853, 2012.
The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; 13(Apr):1059−1062, 2012.
[abs][pdf] [code][cran.r-project.org]
glm-ie: Generalised Linear Models Inference & Estimation Toolbox
Hannes Nickisch; 13(May):1699−1703, 2012.
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; 13(Jun):1967−1971, 2012.
Pattern for Python
Tom De Smedt, Walter Daelemans; 13(Jun):2063−2067, 2012.
DEAP: Evolutionary Algorithms Made Easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné; 13(Jul):2171−2175, 2012.
[abs][pdf] [code][deap.gel.ulaval.ca]
A Topic Modeling Toolbox Using Belief Propagation
Jia Zeng; 13(Jul):2233−2236, 2012.
PREA: Personalized Recommendation Algorithms Toolkit
Joonseok Lee, Mingxuan Sun, Guy Lebanon; 13(Sep):2699−2703, 2012.
Oger: Modular Learning Architectures For Large-Scale Sequential Processing
David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; 13(Oct):2995−2998, 2012.
Sally: A Tool for Embedding Strings in Vector Spaces
Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; 13(Nov):3247−3251, 2012.
DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
Stephen Gould; 13(Dec):3533−3537, 2012.
SVDFeature: A Toolkit for Feature-based Collaborative Filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; 13(Dec):3619−3622, 2012.
AC++Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics
Hervé Frezza-Buet, Matthieu Geist; 14(Feb):625−628, 2013.
[abs][pdf] [code][malis.metz.supelec.fr]
MLPACK: A Scalable C++ Machine Learning Library
Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; 14(Mar):801−805, 2013.
GPstuff: Bayesian Modeling with Gaussian Processes
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari; 14(Apr):1175−1179, 2013.
JKernelMachines: A Simple Framework for Kernel Machines
David Picard, Nicolas Thome, Matthieu Cord; 14(May):1417−1421, 2013.
[abs][pdf][bib] [code][mloss.org]
Orange: Data Mining Toolbox in Python
Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, Blaž Zupan; 14(Aug):2349−2353, 2013.
[abs][pdf][bib] [code][mloss.org]
Tapkee: An Efficient Dimension Reduction Library
Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; 14(Aug):2355−2359, 2013.
[abs][pdf][bib] [code][mloss.org]
The CAM Software for Nonnegative Blind Source Separation in R-Java
Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; 14(Sep):2899−2903, 2013.
[abs][pdf][bib] [code][mloss.org]
QuantMiner for Mining Quantitative Association Rules
Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; 14(Oct):3153−3157, 2013.
[abs][pdf][bib] [code][github.com]
Divvy: Fast and Intuitive Exploratory Data Analysis
Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; 14(Oct):3159−3163, 2013.
[abs][pdf][bib] [code][mloss.org]
GURLS: A Least Squares Library for Supervised Learning
Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; 14(Oct):3201−3205, 2013.
[abs][pdf][bib] [code][github.com]
BudgetedSVM: A Toolbox for Scalable SVM Approximations
Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; 14(Dec):3813−3817, 2013.
[abs][pdf][bib] [code][temple.edu]
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; 15(Jan):141−145, 2014.
[abs][pdf][bib] [code][mloss.org]
Information Theoretical Estimators Toolbox
Zoltán Szabó; 15(Jan):283−287, 2014.
[abs][pdf][bib] [code][mloss.org]
The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R
Haotian Pang, Han Liu, Robert Vanderbei; 15(Feb):489−493, 2014.
[abs][pdf][bib] [code][mloss.org]
LIBOL: A Library for Online Learning Algorithms
Steven C.H. Hoi, Jialei Wang, Peilin Zhao; 15(Feb):495−499, 2014.
[abs][pdf][bib] [code][mloss.org]
Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; 15(Mar):981−1009, 2014.
[abs][pdf][bib] [code][github.com]
Manopt, a Matlab Toolbox for Optimization on Manifolds
Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; 15(Apr):1455−1459, 2014.
[abs][pdf][bib] [code][manopt.org]
pystruct - Learning Structured Prediction in Python
Andreas C. Müller, Sven Behnke; 15(Jun):2055−2060, 2014.
[abs][pdf][bib] [code][github.io]
ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation
Ivo Couckuyt, Tom Dhaene, Piet Demeester; 15(Oct):3183−3186, 2014.
[abs][pdf][bib] [code][sumo.intec.ugent.be]
The Gesture Recognition Toolkit
Nicholas Gillian, Joseph A. Paradiso; 15(Oct):3483−3487, 2014.
[abs][pdf][bib] [code][github.com]
SPMF: A Java Open-Source Pattern Mining Library
Philippe Fournier Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; 15(Nov):3389−3393, 2014.
[abs][pdf][bib] [code][www.philippe-fournier-viger.com]
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Ruben Martinez-Cantin; 15(Nov):3735−3739, 2014.
[abs][pdf][bib] [code][bitbucket.org]
SAMOA: Scalable Advanced Massive Online Analysis
Gianmarco De Francisci Morales, Albert Bifet; 16(Jan):149−153, 2015.
[abs][pdf][bib] [code][samoa-project.net]
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; 16(Mar):553−557, 2015.
[abs][pdf][bib] [code][cran.r-project.org]
Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit
Felix Weninger; 16(Mar):547−551, 2015.
[abs][pdf][bib] [code][sourceforge.net]
A Classification Module for Genetic Programming Algorithms in JCLEC
Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; 16(Mar):491−494, 2015.
[abs][pdf][bib] [code][jclec.sourceforge.net]
