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About: machine learning library in java for easy development of new kernels and kernel algorithms Changes:Version 3.0 /! Warning: this version is incompatible with previous code
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About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by r-cran-robot on 2016-05-01 00:00:04.546512
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About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls. Changes:Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.
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About: A Tool for Measuring String Similarity Changes:This release fixes the incorrect implementation of the bag distance.
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About: TBEEF, a doubly ensemble framework for recommendation and prediction problems. Changes:Included the final technical report.
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About: Analytic engine for real-time large-scale streams containing structured and unstructured data. Changes:Initial Announcement on mloss.org.
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About: Python toolbox for manifold optimization with support for automatic differentiation Changes:Initial Announcement on mloss.org.
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About: Jatecs is an open source Java library focused on automatic text categorization. Changes:Initial Announcement on mloss.org.
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About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods. Changes:Additions and improvements from ELKI 0.7.0 to 0.7.1: Algorithm additions:
Important bug fixes:
UI improvements:
Smaller changes:
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About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs. Changes:Initial Announcement on mloss.org.
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About: testing mloss.org Changes:Initial Announcement on mloss.org.
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About: Local high-order regularization for semi-supervised learning Changes:Initial Announcement on mloss.org.
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About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more. Changes:New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.
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About: Bayesian Additive Regression Trees Changes:Fetched by r-cran-robot on 2018-09-01 00:00:04.269138
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About: Collection of algorithms for Gaussian Processes. Regression, Classification, Multi task, Multi output, Hierarchical, Sparse Changes:Initial Announcement on mloss.org.
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About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees. Changes:Version 1.1.2d (12/29/2015):
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About: a tool for marking samples in images for database building, also including algorithm of LBP,HOG,and classifiers of SVM (six kernels), adaboost,BP and convolutional networks, extreme learning machine. Changes:Initial Announcement on mloss.org.
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About: DiffSharp is a functional automatic differentiation (AD) library providing gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products as higher-order functions. It allows exact and efficient calculation of derivatives, with support for nesting. Changes:Fixed: Bug fix in forward AD implementation of Sigmoid and ReLU for D, DV, and DM (fixes #16, thank you @mrakgr) Improvement: Performance improvement by removing several more Parallel.For and Array.Parallel.map operations, working better with OpenBLAS multithreading Added: Operations involving incompatible dimensions of DV and DM will now throw exceptions for warning the user
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About: A Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems. Changes:
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About: Lua-MapReduce framework implemented in Lua using luamongo driver and MongoDB as storage. It follows Iterative MapReduce for training of Machine Learning statistical models. Changes:
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