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Logo r-cran-arules 1.5-0

by r-cran-robot - September 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 37342 views, 7825 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2017-02-01 00:00:04.072286


Logo RLScore 0.7

by aatapa - September 20, 2016, 09:51:25 CET [ Project Homepage BibTeX Download ] 1143 views, 248 downloads, 3 subscriptions

About: RLScore - regularized least-squares machine learning algorithms package

Changes:

Initial Announcement on mloss.org.


Logo r-cran-bst 0.3-14

by r-cran-robot - September 12, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 7128 views, 1649 downloads, 2 subscriptions

About: Gradient Boosting

Changes:

Fetched by r-cran-robot on 2017-02-01 00:00:05.153083


Logo slim for matlab 0.2

by ustunb - August 23, 2016, 20:27:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2058 views, 331 downloads, 3 subscriptions

About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio

Changes:

Initial Announcement on mloss.org.


Logo KeLP 2.1.0

by kelpadmin - August 11, 2016, 10:40:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13076 views, 2913 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 prediction models without writing a single line of code.

Changes:

In addition to minor bug fixes, this release includes:

  • a flexible system to manipulate example-pairs
  • new manipulators for performing tree pruning
  • new examples for the usage of kelp

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 2.1.0!


Logo Sparse Compositional Metric Learning v1.11

by bellet - August 2, 2016, 11:43:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 5763 views, 1729 downloads, 3 subscriptions

About: Scalable learning of global, multi-task and local metrics from data

Changes:

Minor bug fix in multi-task objective computation (thanks to Junjie Hu).


Logo Social Impact theory based Optimizer library 1.1

by rishem - July 29, 2016, 13:19:47 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9629 views, 1919 downloads, 2 subscriptions

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.

Changes:

bug removed


Logo r-cran-CORElearn 1.48.0

by r-cran-robot - July 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 15616 views, 3316 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation

Changes:

Fetched by r-cran-robot on 2017-02-01 00:00:06.399901


About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 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. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. 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 based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website.

Changes:

New in toolbox

  • Using GPU in Backpropagation
  • Revision of some demo scripts
  • Function approximation with multiple outputs
  • Feature extraction with GRBM in first layer

cardinal


Logo SparklingGraph 0.0.6

by riomus - June 17, 2016, 14:49:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4450 views, 911 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.

Changes:

Bug fixes, Graph generators


Showing Items 91-100 of 635 on page 10 of 64: First Previous 5 6 7 8 9 10 11 12 13 14 15 Next Last