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Logo revrand 0.6.0

by dsteinberg - August 8, 2016, 08:39:08 CET [ Project Homepage BibTeX Download ] 5729 views, 1059 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 Sparse Compositional Metric Learning v1.11

by bellet - August 2, 2016, 11:43:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 4751 views, 1466 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 r-cran-BayesTree 0.3-1.4

by r-cran-robot - February 21, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 9079 views, 2027 downloads, 1 subscription

About: Bayesian Additive Regression Trees

Changes:

Fetched by r-cran-robot on 2016-09-01 00:00:04.084331


Logo r-cran-CORElearn 1.48.0

by r-cran-robot - July 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 13095 views, 2873 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation

Changes:

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


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 ] 8120 views, 1641 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 JMLR scikitlearn 0.17.1

by fabianp - July 28, 2016, 20:05:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22846 views, 8586 downloads, 4 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 3 votes)

About: The scikit-learn project is a machine learning library in Python.

Changes:

Update for 0.17.1


Logo JMLR MLPACK 2.0.3

by rcurtin - July 22, 2016, 00:39:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 64567 views, 11653 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Standardize some parameter names for programs (old names are kept for reverse compatibility, but warnings will now be issued).
  • RectangleTree optimizations (#721).
  • Fix memory leak in NeighborSearch (#731).
  • Documentation fix for k-means tutorial (#730).
  • Fix TreeTraits for BallTree (#727).
  • Fix incorrect parameter checks for some command-line programs.
  • Fix error in HMM training with probabilities for each point (#636).

Logo Armadillo library 7.200

by cu24gjf - July 10, 2016, 15:44:07 CET [ Project Homepage BibTeX Download ] 90656 views, 18127 downloads, 5 subscriptions

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About: Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

Changes:
  • eigs_sym(), eigs_gen() and svds() now use a built-in reimplementation of ARPACK; contributed by Yixuan Qiu
  • faster handling of compound expressions by vectorise()
  • added .index_min() and .index_max()
  • added erf(), erfc(), lgamma()
  • added .head_slices() and .tail_slices() to subcube views
  • expanded ind2sub() to handle vectors of indices
  • expanded sub2ind() to handle matrix of subscripts
  • expanded expmat(), logmat() and sqrtmat() to optionally return a bool indicating success
  • spsolve() now requires SuperLU 5.2

Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 5451 views, 1255 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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