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Showing Items 61-70 of 628 on page 7 of 63: First Previous 2 3 4 5 6 7 8 9 10 11 12 Next Last

Logo JMLR DLLearner 1.0

by Jens - February 13, 2015, 11:39:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23269 views, 5170 downloads, 6 subscriptions

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About: The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL.

Changes:

See http://dl-learner.org/development/changelog/.


Logo JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23244 views, 7205 downloads, 1 subscription

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23198 views, 4890 downloads, 3 subscriptions

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):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!

Logo Apache Mahout 0.11.1

by gsingers - November 9, 2015, 16:12:06 CET [ Project Homepage BibTeX Download ] 23102 views, 5932 downloads, 3 subscriptions

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Changes:

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.


Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 23051 views, 8702 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

Changes:

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.


Logo hca 0.63

by wbuntine - April 26, 2016, 15:35:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22606 views, 3243 downloads, 4 subscriptions

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.


Logo JMLR Nieme 1.0

by francis - April 2, 2009, 10:57:38 CET [ Project Homepage BibTeX Download ] 22316 views, 2987 downloads, 1 subscription

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About: Nieme is a C++ machine learning library for large-scale classification, regression and ranking. It provides a simple interface available in C++, Python and Java and a user interface for visualization.

Changes:

Released Nieme 1.0


Logo r-cran-glmnet 1.9-3

by r-cran-robot - March 1, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 21898 views, 4669 downloads, 1 subscription

About: Lasso and elastic-net regularized generalized linear models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.081872


Logo Somoclu 1.7.2

by peterwittek - November 24, 2016, 22:43:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 21722 views, 3937 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

Changes:
  • New: The coefficient of the Gaussian neighborhood function exp(-||x-y||^2/(2(coeffradius)^2)) is now exposed in all interfaces as a parameter.
  • New: get_bmu function in the Python interface to get the best matching units given an activation map.
  • Changed: Updated PCA initialization in the Python interface to work with sk-learn 0.18 onwards.
  • Changed: Radii can be float values.
  • Fixed: Only positive values were written back to codebook during update.
  • Fixed: Sparse data is read correctly when there are class labels.

Logo JMLR RL Glue and Codecs -- Glue 3.x and Codecs

by btanner - October 12, 2009, 07:50:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 21646 views, 2416 downloads, 1 subscription

About: RL-Glue allows agents, environments, and experiments written in Java, C/C++, Matlab, Python, and Lisp to inter operate, accelerating research by promoting software re-use in the community.

Changes:

RL-Glue paper has been published in JMLR.


Showing Items 61-70 of 628 on page 7 of 63: First Previous 2 3 4 5 6 7 8 9 10 11 12 Next Last