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Logo KeLP 2.1.0

by kelpadmin - August 11, 2016, 10:40:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10671 views, 2472 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 AMIDST Toolbox 0.5.1

by ana - August 8, 2016, 13:45:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3714 views, 534 downloads, 4 subscriptions

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

Changes:

The new added functionalities include support to Flink for distributed learning of probabilistic graphical models and support for Latent Dirichlet Allocation Models for text analysis purposes.

Detailed information can be found in the toolbox's web page


Logo revrand 0.6.0

by dsteinberg - August 8, 2016, 08:39:08 CET [ Project Homepage BibTeX Download ] 5843 views, 1088 downloads, 3 subscriptions

About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • The GLM now uses Auto-encoding variational Bayes for inference as opposed to nonparametric variational inference. This substantially improves performance and simplifies the codebase.
  • Many bugfixes.

Logo r-cran-arules 1.4-2

by r-cran-robot - August 6, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 32271 views, 6887 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2016-09-01 00:00:03.643479


Logo Sparse Compositional Metric Learning v1.11

by bellet - August 2, 2016, 11:43:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 4804 views, 1473 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 ] 8185 views, 1648 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-caret 6.0-71

by r-cran-robot - July 24, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 102324 views, 19916 downloads, 3 subscriptions

About: Classification and Regression Training

Changes:

Fetched by r-cran-robot on 2016-09-01 00:00:04.636086


Logo r-cran-CORElearn 1.48.0

by r-cran-robot - July 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 13230 views, 2888 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation

Changes:

Fetched by r-cran-robot on 2016-09-01 00:00:05.421251


Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 5516 views, 1269 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

Changes:
  • Add Logistic Regression
  • Add support for sparse input in fast training of linear SVM
  • Better support for sparse vectors/matrices
  • Fix plot windows in IE
  • Minor bug fixes

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


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