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Logo KeLP 2.0.0

by kelpadmin - November 26, 2015, 16:14:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3881 views, 972 downloads, 3 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code.


This is a major release that includes brand new features as well as a renewed architecture of the entire project.

Now KeLP is organized in four maven projects:

  • kelp-core: it contains the infrastructure of abstract classes and interfaces to work with KeLP. Furthermore, some implementations of algorithms, kernels and representations are included, to provide a base operative environment.

  • kelp-additional-kernels: it contains several kernel functions that extend the set of kernels made available in the kelp-core project. Moreover, this project implements the specific representations required to enable the application of such kernels. In this project the following kernel functions are considered: Sequence kernels, Tree kernels and Graphs kernels.

  • kelp-additional-algorithms: it contains several learning algorithms extending the set of algorithms provided in the kelp-core project, e.g. the C-Support Vector Machine or ν-Support Vector Machine learning algorithms. In particular, advanced learning algorithms for classification and regression can be found in this package. The algorithms are grouped in: 1) Batch Learning, where the complete training dataset is supposed to be entirely available during the learning phase; 2) Online Learning, where individual examples are exploited one at a time to incrementally acquire the model.

  • kelp-full: this is the complete package of KeLP. It aggregates the previous modules in one jar. It contains also a set of fully functioning examples showing how to implement a learning system with KeLP. Batch learning algorithm as well as Online Learning algorithms usage is shown here. Different examples cover the usage of standard kernel, Tree Kernels and Sequence Kernel, with caching mechanisms.

Furthermore this new release includes:

  • CsvDatasetReader: it allows to read files in CSV format

  • DCDLearningAlgorithm: it is the implementation of the Dual Coordinate Descent learning algorithm

  • methods for checking the consistency of a dataset.

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.0!

Logo PROFET 1.0.0

by Hamda - November 26, 2015, 13:20:28 CET [ Project Homepage BibTeX Download ] 74 views, 12 downloads, 1 subscription

About: Software for Automatic Construction and Inference of DBNs Based on Mathematical Models


Initial Announcement on

Logo A Library for Online Streaming Feature Selection 1.0

by ykui713 - November 25, 2015, 13:23:01 CET [ BibTeX Download ] 110 views, 26 downloads, 0 subscriptions

About: LOFS is a software toolbox for online streaming feature selection


Initial Announcement on

Logo PyScriptClassifier 0.3.0

by cjb60 - November 25, 2015, 04:07:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 828 views, 220 downloads, 1 subscription

About: Easily prototype WEKA classifiers and filters using Python scripts.



  • Filters have now been implemented.
  • Classifier and filter classes satisfy base unit tests.


  • Can now choose to save the script in the model using the -save flag.


  • Added Python 3 support.
  • Added uses decorator to prevent non-essential arguments from being passed.
  • Fixed nasty bug where imputation, binarisation, and standardisation would not actually be applied to test instances.
  • GUI in WEKA now displays the exception as well.
  • Fixed bug where single quotes in attribute values could mess up args creation.
  • ArffToPickle now recognises class index option and arguments.
  • Fix nasty bug where filters were not being saved and were made from scratch from test data.


  • ArffToArgs gets temporary folder in a platform-independent way, instead of assuming /tmp/.
  • Can now save args in ArffToPickle using save.


  • Initial release.

Logo bandicoot 0.4

by yvesalexandre - November 20, 2015, 17:08:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 252 views, 46 downloads, 2 subscriptions

About: An open-source Python toolbox to analyze mobile phone metadata.


Initial Announcement on

Logo ADAMS 0.4.11

by fracpete - November 18, 2015, 10:58:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15339 views, 3079 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.


Some highlights of this release:

  • switch to Java 8
  • preferred IDE is now IntelliJ IDEA
  • removed OSX builds
  • 43 new actors
  • 13 new conversions
  • removed obsolete actors and conversions
  • added video support (video files and webcams)
  • added object detection and tracking (incl recording of object trails)
  • proof-of-concept remote-execution of jobs
  • SSH console
  • support for webscraping using JSoup
  • MEKA upgraded to 1.9.0
  • MOA regressor support added
  • better syntax highlighting for Groovy/Jython
  • several new Weka classifiers (eg Veto, LeanMultiScheme, ThresholdedBinaryClassification, InputSmearing)
  • new genetic algorithm: Hermione
  • extended the abstaining classifier framework (integrates with Weka)
  • adams-imaging split into: adams-imaging, adams-boofcv, adams-imagemagick, adams-imagej, adams-openimaj (newly added)

Logo Deep Semantic Ranking Based Hashing 1.0

by openpr_nlpr - November 18, 2015, 07:25:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 289 views, 71 downloads, 2 subscriptions

About: This algorithm is described in Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval. See


Initial Announcement on

Logo Hype 0.1.0

by gbaydin - November 16, 2015, 18:35:57 CET [ Project Homepage BibTeX Download ] 248 views, 38 downloads, 3 subscriptions

About: Hype is a proof-of-concept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization.


Initial Announcement on

Logo Armadillo library 6.200

by cu24gjf - November 15, 2015, 06:54:50 CET [ Project Homepage BibTeX Download ] 68517 views, 13978 downloads, 5 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

  • expanded diagmat() to handle non-square matrices and arbitrary diagonals
  • expanded trace() to handle non-square matrices
  • correction for datum::Z_0 constant
  • bug fixes for sparse eigen decomposition

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more


This version comes with Distributed and Mobile Examples

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