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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 21710 views, 8440 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

Changes:

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.


Logo Local high order regularization 1.0

by kkim - March 2, 2016, 13:46:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 749 views, 174 downloads, 2 subscriptions

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About: Local high-order regularization for semi-supervised learning

Changes:

Initial Announcement on mloss.org.


Logo lomo feature extraction and xqda metric learning for person reidentification 1.0

by openpr_nlpr - May 6, 2015, 11:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2329 views, 347 downloads, 3 subscriptions

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About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-CoxBoost 1.4

by r-cran-robot - July 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 26044 views, 5267 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-Boruta 5.0.0

by r-cran-robot - July 1, 2016, 00:00:03 CET [ Project Homepage BibTeX Download ] 17246 views, 3746 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 ] 4005 views, 979 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 ] 142380 views, 23208 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 ] 2907 views, 571 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 ] 2073 views, 370 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.

Changes:

Bug fixes, Graph generators


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