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Showing Items 1-10 of 622 on page 1 of 63: 1 2 3 4 5 6 Next Last

Logo JMLR MLPACK 2.0.3

by rcurtin - July 22, 2016, 00:39:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 61297 views, 11170 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 ] 86204 views, 17434 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 r-cran-CoxBoost 1.4

by r-cran-robot - July 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 26621 views, 5351 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks

Changes:

Fetched by r-cran-robot on 2016-07-01 00:00:04.841105


Logo r-cran-e1071 1.6-7

by r-cran-robot - July 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 26497 views, 5714 downloads, 3 subscriptions

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About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly

Changes:

Fetched by r-cran-robot on 2016-07-01 00:00:04.963467


Logo r-cran-Boruta 5.0.0

by r-cran-robot - July 1, 2016, 00:00:03 CET [ Project Homepage BibTeX Download ] 17787 views, 3824 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2016-07-01 00:00:03.793481


Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 4693 views, 1090 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


Logo JMLR dlib ml 19.0

by davis685 - June 25, 2016, 23:04:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 145263 views, 23566 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds a deep learning toolkit to dlib that has a clean and fully documented C++11 API. It also includes CPU and GPU support, binds to cuDNN, can train on multiple GPUs at a time, and comes with a pretrained imagenet model based on ResNet34.

The release also adds a number of other improvements such as new elastic net regularized solvers and QP solvers, improved MATLAB binding tools, and other usability tweaks and optimizations.


Logo revrand 0.4.1

by dsteinberg - June 24, 2016, 05:58:05 CET [ Project Homepage BibTeX Download ] 3548 views, 698 downloads, 3 subscriptions

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

Changes:
  • Allow for non-learnable likelihood arguments (per datum) in the glm
  • Hotfix for glm prediction sampling functions

Logo SparklingGraph 0.0.6

by riomus - June 17, 2016, 14:49:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2643 views, 488 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.

Changes:

Bug fixes, Graph generators


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