MLOSS 2013

11 papers

A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics Hervé Frezza-Buet, Matthieu Geist

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BudgetedSVM: A Toolbox for Scalable SVM Approximations Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang

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Divvy: Fast and Intuitive Exploratory Data Analysis Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten

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GPstuff: Bayesian Modeling with Gaussian Processes Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari

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GURLS: A Least Squares Library for Supervised Learning Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco

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JKernelMachines: A Simple Framework for Kernel Machines David Picard, Nicolas Thome, Matthieu Cord

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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

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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

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QuantMiner for Mining Quantitative Association Rules Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard

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Tapkee: An Efficient Dimension Reduction Library Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia

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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

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