RSS Feed - Blog
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About: Toolkit for parametric and nonparametric regression and classification. Changes:Initial Announcement on mloss.org.
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About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster. Changes:Initial Announcement on mloss.org.
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About: A Java Toolbox for Scalable Probabilistic Machine Learning. Changes:
Detailed information can be found in the toolbox's web page
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About: RLScore - regularized least-squares machine learning algorithms package Changes:Initial Announcement on mloss.org.
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About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio Changes:Initial Announcement on mloss.org.
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About: Scalable learning of global, multi-task and local metrics from data Changes:Minor bug fix in multi-task objective computation (thanks to Junjie Hu).
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About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA. Changes:bug removed
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About: Package for Deep Architectures and Restricted Boltzmann Machines Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.467914
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About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website. Changes:New in toolbox
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About: A Content Anomaly Detector based on n-Grams Changes:A teeny tiny fix to correctly handle input strings shorter than a registers width
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About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. Changes:
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About: The Multiagent decision process (MADP) Toolbox is a free C++ software toolbox for scientific research in decision-theoretic planning and learning in multiagent systems. Changes:-Includes freshly written spirit parser for .pomdp files. -Includes new code for pruning POMDP vectors; obviates dependence on Cassandra's code and old LP solve version. -Includes new factor graph solution code -Generalized firefighting CGBG domain added -Simulation class for Factored Dec-POMDPs and TOI Dec-MDPs -Approximate BG clustering methods and kGMAA with clustering.
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About: Automatically finds the best model with its best parameter settings for a given classification or regression task. Changes:Initial Announcement on mloss.org.
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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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