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Logo dANN AI Library 1.3

by freemo - April 25, 2010, 05:36:46 CET [ Project Homepage BibTeX Download ] 5062 views, 1207 downloads, 1 subscription

About: dANN is an Artificial Intelligence and Artificial Genetics library targeted at employing conventional techniques as well as acting as a platform for research & development of novel techniques. As new techniques are developed and proven to be effective they will be integrated into the core library. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it directly from GIT.


Please get the version in GIT only, the released version is old.

Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40039 views, 8324 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.


Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

Logo dataformat 0.1.1

by mikio - March 12, 2009, 16:07:55 CET [ Project Homepage BibTeX Download ] 7893 views, 1527 downloads, 2 subscriptions

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.


Forgot to include the Java sources.

Logo DCABags 0.7

by wbuntine - June 5, 2014, 05:34:44 CET [ Project Homepage BibTeX Download ] 4142 views, 951 downloads, 4 subscriptions

About: Document/Text preprocessing for topic models: suite of Perl scripts for preprocessing text collections to create dictionaries and bag/list files for use by topic modelling software.


Moved distribution and code across to GitHub. Changed "ldac" format to have 0 offset for word indices. Added "document frequency" (df) filtering on selection of tokens for linkTables. Playing with linkParse but its still unuseable generally.

Logo Debellor 1.0

by mwojnars - July 30, 2009, 16:48:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8215 views, 2272 downloads, 1 subscription

About: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

  • Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
  • Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
  • New class: CrossValidation - evaluator of trainable cells through cross-validation
  • New class: RMSE - calculation of Root Mean Squared Error score
  • Data objects can be compared and used in collections
  • ArffReader can read from a user-provided
  • More convenient use of parameters (setting values)
  • More convenient use of data objects and data types (construction, type casting)
  • Other minor improvements to existing classes
  • Javadoc extended

About: Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 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. 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, etc. The toolbox is a user-friendly open source software and is freely available on the website.


New in toolbox

  • Bug was fixed for computeBatchSize function in Linux.
  • Revision of some demo scripts. cardinal

Logo deepdetect 0.1

by beniz - June 2, 2015, 09:25:28 CET [ Project Homepage BibTeX Download ] 839 views, 240 downloads, 3 subscriptions

About: A Deep Learning API and server


Initial Announcement on

Logo DeltaLDA 0.1.1

by davidandrzej - July 16, 2009, 21:52:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8117 views, 1442 downloads, 1 subscription

About: This software implements the DeltaLDA model, which is a modification of the Latent Dirichlet Allocation (LDA) model. DeltaLDA can use multiple topic mixing weight priors to jointly model multiple [...]


-fixed some npy_intp[] memory leaks -fixed phi normalization bug

Logo Denoising TOF 3D images using confidence measures 1

by mafrank - August 17, 2011, 16:59:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3714 views, 773 downloads, 1 subscription

About: Denoising images via normalized convolution


Initial Announcement on

Logo Dependency modeling toolbox 0.2

by lml - April 30, 2010, 14:38:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8525 views, 1327 downloads, 1 subscription

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.


Three independent modules (drCCA, pint, MultiWayCCA) have been added.

Showing Items 91-100 of 599 on page 10 of 60: First Previous 5 6 7 8 9 10 11 12 13 14 15 Next Last