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Logo MLweb 0.1.2

by lauerfab - October 9, 2015, 11:55:52 CET [ Project Homepage BibTeX Download ] 1476 views, 404 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 Regression:AutoReg method
  • Add KernelRidgeRegression tuning function
  • More efficient predictions for KRR, SVM, SVR
  • Add BFGS optimization method
  • Faster QR, SVD and eigendecomposition
  • Better support for sparse vectors and matrices
  • Add linear algebra benchmark at
  • Fix plots in LALOlib/ML.js
  • Fix cross-origin issues in new MLlab()
  • Small bug fixes

Logo NPD Face Detector Training 1.0

by openpr_nlpr - October 8, 2015, 04:22:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 707 views, 160 downloads, 2 subscriptions

About: This MATLAB package provides the Deep Quadratic Tree (DQT) and the Normalized Pixel Difference (NPD) based face detector training method proposed in our PAMI 2015 paper. It is fast, and effective for unconstrained face detection. For more details, please visit


Initial Announcement on

Logo r-cran-CORElearn 1.47.1

by r-cran-robot - September 3, 2015, 00:00:00 CET [ Project Homepage BibTeX Download ] 8801 views, 2088 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation


Fetched by r-cran-robot on 2015-12-01 00:00:06.157088

Logo r-cran-Boruta 5.0.0

by r-cran-robot - December 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 14041 views, 2984 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection


Fetched by r-cran-robot on 2015-12-01 00:00:05.244246

Logo Somoclu 1.5

by peterwittek - September 30, 2015, 13:27:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10156 views, 1998 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, R, and MATLAB are supported.

  • New: Python interface has visual capabilities.
  • New: Option for hexagonal grid.
  • New: Option for requesting compact support in updating the map.
  • New: Python, R, and MATLAB interfaces now allow passing an initial codebook.
  • Changed: Reduced memory use in calculating U-matrices.
  • Changed: Build system rebuilt and simplified.

Logo Optunity 1.1.1

by claesenm - September 30, 2015, 07:06:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4271 views, 1039 downloads, 3 subscriptions

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.


This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

Logo Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 4177 views, 1046 downloads, 2 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.


Initial Announcement on

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 541 views, 87 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.


Initial Announcement on

Logo python weka wrapper 0.3.3

by fracpete - September 26, 2015, 06:11:42 CET [ Project Homepage BibTeX Download ] 19407 views, 4131 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

  • updated to Weka 3.7.13
  • documentation now covers the API as well

Logo KEEL Knowledge Extraction based on Evolutionary Learning 3.0

by keel - September 18, 2015, 12:38:54 CET [ Project Homepage BibTeX Download ] 623 views, 188 downloads, 1 subscription

About: KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and educational. KEEL is also coupled with KEEL-dataset: a webpage that aims at providing to the machine learning researchers a set of benchmarks to analyze the behavior of the learning methods. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semi-supervised classification, regression, time series and unsupervised learning. Also, a set of low quality data benchmarks is maintained in the repository.


Initial Announcement on

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