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About: The goal of this project is to provide code for reading and writing machine learning data sets for as many programming languages as possible. Changes:Forgot to include the Java sources.
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About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Changes:Initial Announcement on mloss.org.
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About: C-MixSim is an open source package written in C for simulating finite mixture models with Gaussian components. With a vast number of clustering algorithms, evaluating performance is important. C-MixSim provides an easy and convenient way of generating datasets from Gaussian mixture models with different levels of clustering complexity. C-MixSim is released under the GNU GPL license. Changes:Initial Announcement on mloss.org.
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About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...] Changes:Initial Announcement on mloss.org.
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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM Changes:Initial Announcement on mloss.org.
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About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features. Changes:Initial Announcement on mloss.org.
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About: Regularization paths for regression models with grouped covariates Changes:Fetched by r-cran-robot on 2013-04-01 00:00:05.489694
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About: Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The demand for such methods is increasing with the availability and size of co-occurring observations in computational biology, open data initiatives, and in other domains. We provide practical, open access implementations of general-purpose algorithms that help to realize the full potential of these information sources. Changes:Three independent modules (drCCA, pint, MultiWayCCA) have been added.
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About: Genetic Algorithm for Curve Fitting Changes:Fetched by r-cran-robot on 2012-10-01 00:00:04.684941
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About: Hubness-aware Machine Learning for High-dimensional Data Changes:
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About: Sequin is an open source sequence mining library written in C#. Changes:Sequin v1.1.0.0 released
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About: Multicore/distributed large scale machine learning framework. Changes:Update version.
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About: An audio editing tool for single-channel source separation. Changes:Stereo processing, bug fixes, UI updates.
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About: TinyOS is a small operating for small (wireless) sensors. LEGO MINDSTORMS NXT is a platform for embedded systems experimentation: The combination of NXT and TinyOS is NXTMOTE. Changes:Initial Announcement on mloss.org.
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About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Changes:
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About: Likelihood-based Boosting for Generalized mixed models Changes:Fetched by r-cran-robot on 2013-04-01 00:00:05.366545
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About: OpenCog aims to provide research scientists and software developers with a common platform to build and share artificial intelligence programs. The long-term goal of OpenCog is acceleration of the [...] Changes:Initial Announcement on mloss.org.
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About: A Graphical Tool for Designing and Training Deep Neural Networks Changes:
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About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included. Changes:Spectrum LSTM package included
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About: This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares. Changes:
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