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About: Lasso and elastic-net regularized generalized linear models Changes:Fetched by r-cran-robot on 2013-04-01 00:00:05.081872
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About: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.939105
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About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini. Changes:
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About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications. Changes:
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About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. Changes:Learners
Measures/Evaluation
Bug fixes
API changes
Miscalleneous
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About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Major changes :
Minor fixes:
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About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs). Changes:compilation problems fixed
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About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used
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About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:-Fixed bug in save/restore. -Fixed bug in initial design.
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About: Classification, Regression and Feature Evaluation Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.164852
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About: A Java framework for statistical analysis and classification of biological sequences Changes:New classes and packages:
New features and improvements:
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About: Python Framework for Vector Space Modelling that can handle unlimited datasets (streamed input, online algorithms work incrementally in constant memory). Changes:
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About: peewit provides services for programming, running and result examination of machine learning experiments. It does not include any ML algorithms, has no GUI, and presumes certain uniformity of the experimental layout. But it does not make assumptions on the type of task under study. The current version-number is 0.10. Changes:v-cube with side-cubes
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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: 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 library of scalable Bayesian generalised linear models with fancy features Changes:
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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: CARP: The Clustering Algorithms’ Referee Package Changes:Generalized overlap error and some bugs have been fixed
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About: Pam Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.709586
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About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification. Changes:In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows: • Add a template and guide for adding new algorithms; • Improve parameter settings and make documentation clear; • Improve documentation on data formats and key functions; • Amend the "OGD" function to use different loss types; • Fixed some name inconsistency and other minor bugs.
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