Projects running under linux.
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Logo epac 0.10

by jinpengli - October 9, 2013, 14:00:15 CET [ Project Homepage BibTeX Download ] 2768 views, 724 downloads, 1 subscription

About: Embarrassingly Parallel Array Computing: EPAC is a machine learning workflow builder.

Changes:

Initial Announcement on mloss.org.


Logo MyMediaLite 3.10

by zenog - October 8, 2013, 22:29:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 67130 views, 12330 downloads, 1 subscription

About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

Changes:

Mostly bug fixes.

For details see: https://github.com/zenogantner/MyMediaLite/blob/master/doc/Changes


Logo JMLR scikitlearn 0.14.1

by fabianp - October 4, 2013, 15:01:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20418 views, 7770 downloads, 3 subscriptions

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

Changes:

Update for 0.14.1


Logo OpenANN 1.1.0

by afabisch - September 26, 2013, 23:52:03 CET [ Project Homepage BibTeX Download ] 5207 views, 1073 downloads, 2 subscriptions

About: A library for artificial neural networks.

Changes:

Added algorithms:

  • L-BFGS optimizer
  • k-means
  • sparse auto-encoder
  • preprocessing: normalization, PCA, ZCA whitening

Logo Ankus 0.0.1

by suhyunjeon - September 13, 2013, 06:47:46 CET [ Project Homepage BibTeX Download ] 4529 views, 721 downloads, 1 subscription

About: Ankus is an open source data mining / machine learning based MapReduce that supports a variety of advanced algorithms.

Changes:

Initial Announcement on mloss.org.


Logo Rchemcpp 1.99.0

by klambaue - September 10, 2013, 09:10:13 CET [ Project Homepage BibTeX Download ] 5409 views, 1352 downloads, 1 subscription

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About: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules.

Changes:

Moved from CRAN to Bioconductor. Improved handling of molecules, visualization and examples.


Logo Multilinear Principal Component Analysis 1.3

by hplu - September 8, 2013, 13:04:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6812 views, 1304 downloads, 1 subscription

About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition

Changes:
  1. The MPCA paper is updated with a typo (the MAD measure in Table II) corrected.

  2. Tensor toolbox version 2.1 is included for convenience.

  3. Full code on gait recognition is included for verification and comparison.


Logo NuPIC 0.1

by rhyolight - August 21, 2013, 21:01:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2441 views, 1039 downloads, 1 subscription

About: The mission of this project is to build and support a community interested in machine learning and machine intelligence based on modeling the neocortex and the principles upon which it works.

Changes:

Initial Announcement on mloss.org.


Logo Evaluation toolkit 1.0

by openpr_nlpr - August 13, 2013, 08:58:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2658 views, 583 downloads, 1 subscription

About: This evaluation toolkit provides a unified framework for evaluating bag-of-words based encoding methods over several standard image classification datasets.

Changes:

Initial Announcement on mloss.org.


Logo LibBi 1.0.0

by lawmurray - June 23, 2013, 09:04:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2931 views, 729 downloads, 1 subscription

About: Bayesian state-space modelling and inference on high-performance computer hardware.

Changes:

Initial Announcement on mloss.org.


About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level.

Changes:

Initial Announcement on mloss.org.


About: A fast and robust learning of Bayesian networks

Changes:

Initial Announcement on mloss.org.


Logo HLearn 1.0

by mikeizbicki - May 9, 2013, 05:58:18 CET [ Project Homepage BibTeX Download ] 5237 views, 1363 downloads, 1 subscription

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure

Changes:

Updated to version 1.0


Logo KMLib sparse GPU SVM 0.1

by ksopyla - March 20, 2013, 14:30:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3480 views, 909 downloads, 1 subscription

About: Support Vectors Machine library in .net with CUDA support. Library includes GPU SVM solver for kernels linear,RBF,Chi-Square and Exp Chi-Square which use NVIDIA CUDA technology. It allows for classification of feature rich sparse datasets through utilization of sparse matrix formats CSR, Ellpack-R or Sliced EllR-T

Changes:

Initial Announcement on mloss.org.


Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 26981 views, 6051 downloads, 2 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

Changes:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)


Logo Orange 2.6

by janez - February 14, 2013, 18:15:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16559 views, 3221 downloads, 1 subscription

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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

Changes:

The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.

Changed the BibTeX reference to the paper recently published in JMLR MLOSS.


About: SVDFeature is a toolkit for developing generic collaborative filtering algorithms by defining features.

Changes:

JMLR MLOSS version.


Logo UniverSVM 1.22

by fabee - October 16, 2012, 11:24:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23830 views, 3597 downloads, 0 subscriptions

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Changes:

Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser - ferdinand.kaiser@tut.fi)


Logo MROGH 1.0

by openpr_nlpr - October 16, 2012, 04:41:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3429 views, 734 downloads, 1 subscription

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm

Changes:

Initial Announcement on mloss.org.


Logo TurboParser 2.0

by afm - October 11, 2012, 02:59:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6179 views, 1412 downloads, 1 subscription

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

Changes:

This version introduces a number of new features:

  • The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

  • The parser now outputs dependency labels along with the backbone structure.

  • As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

  • The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.


Showing Items 101-120 of 263 on page 6 of 14: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last