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Logo JMLR DLLearner 1.0

by Jens - February 13, 2015, 11:39:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25450 views, 5472 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 Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26167 views, 5459 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 JMLR Surrogate Modeling Toolbox 7.0.2

by dgorissen - September 4, 2010, 07:48:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19428 views, 5416 downloads, 1 subscription

About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive.

Changes:

Incremental update, fixing some cosmetic issues, coincides with JMLR publication.


Logo r-cran-Boruta 5.2.0

by r-cran-robot - June 1, 2017, 00:00:04 CET [ Project Homepage BibTeX Download ] 26251 views, 5283 downloads, 2 subscriptions

About: Wrapper Algorithm for All Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2017-06-01 00:00:04.380348


Logo ADAMS 16.12.1

by fracpete - December 22, 2016, 05:24:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27975 views, 5172 downloads, 3 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:

Some highlights:

  • Over 80 new actors, nearly 30 new conversions
  • Weka Investigator -- the big brother of the Weka Explorer, or how to be more efficient with less clicks using multiple datasets in multiple sessions and multiple predefined outputs per evaluation run
  • Weka Multi-Experimenter -- simple interface for running Weka and ADAMS experiments.
  • File commander -- dual-pane file manager (inspired by Norton/Midnight commander) that allows you to manage local and remote files (ftp, sftp, smb); usually faster than native file managers (like Windows Explorer, Nautilus, Caja) in terms of handling 10s of thousand of files in a single directory
  • experimental deeplearning4j module
  • module for querying/consuming webservices using Groovy
  • basic terminal-based GUI for remote machines (eg cloud)
  • many interactive actors can be used in headless environment now as well
  • Fixed a memory leak introduced by Java's logging framework
  • Flow editor now has predefined rules for swapping actors, e.g. Trigger with Tee or ConditionalTrigger, maintaining as many options as possible (including any sub-actors).
  • improved imaging and PDF support

Logo r-cran-glmnet 1.9-3

by r-cran-robot - March 1, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 24232 views, 5126 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 1.7.4

by peterwittek - June 6, 2017, 15:48:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27564 views, 5002 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: Verbosity parameter in the command-line, Python, MATLAB, and Julia interfaces.
  • Changed: Calculation of U-matrix parallelized.
  • Changed: Moved feeding data to train method in the Python interface.
  • Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.
  • Fixed: Sparse matrix reader made more robust.
  • Fixed: Compatibility with kohonen 3 resolved.
  • Fixed: Compatibility with Matplotlib 2 resolved.

Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15506 views, 4972 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Logo BRML toolbox 070711

by DavidBarber - July 17, 2011, 19:30:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 65765 views, 4919 downloads, 1 subscription

About: Bayesian Reasoning and Machine Learning toolbox

Changes:

Fixed some small bugs and updated some demos.


Logo figtree 0.9.2

by vmorariu - January 17, 2009, 00:13:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6257 views, 4796 downloads, 1 subscription

About: A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and a tree data structure. This library is useful for efficient Kernel Density [...]

Changes:

Initial Announcement on mloss.org.


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