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Showing Items 21-30 of 626 on page 3 of 63: Previous 1 2 3 4 5 6 7 8 Next Last

Logo r-cran-caret 6.0-71

by r-cran-robot - July 24, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 104917 views, 20244 downloads, 3 subscriptions

About: Classification and Regression Training


Fetched by r-cran-robot on 2016-10-01 00:00:04.035474

Logo r-cran-CORElearn 1.48.0

by r-cran-robot - July 23, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 13829 views, 2996 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation


Fetched by r-cran-robot on 2016-10-01 00:00:04.107331

Logo JMLR MLPACK 2.0.3

by rcurtin - July 22, 2016, 00:39:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 66493 views, 11910 downloads, 6 subscriptions

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

  • 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 ] 93458 views, 18569 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).

  • 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 ] 5817 views, 1334 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.

  • 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.


New in toolbox

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


Logo SparklingGraph 0.0.6

by riomus - June 17, 2016, 14:49:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3562 views, 696 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.


Bug fixes, Graph generators

Logo Salad 0.6.1

by chwress - June 17, 2016, 11:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12788 views, 2353 downloads, 3 subscriptions

About: A Content Anomaly Detector based on n-Grams


A teeny tiny fix to correctly handle input strings shorter than a registers width

Logo scikit multilearn 0.0.3

by niedakh - June 15, 2016, 19:28:32 CET [ Project Homepage BibTeX Download ] 976 views, 238 downloads, 2 subscriptions

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.


Initial Announcement on

Logo ADENINE 0.1.3

by samuelefiorini - June 13, 2016, 11:10:36 CET [ Project Homepage BibTeX Download ] 900 views, 176 downloads, 2 subscriptions

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, dimensionality reduction and clustering tasks.


Initial Announcement on

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