Projects running under macosx.
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Logo JMLR Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4141 views, 1103 downloads, 0 subscriptions

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.

Changes:

Initial Announcement on mloss.org.


Logo JMLR dlib ml 18.7

by davis685 - April 10, 2014, 01:47:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 69249 views, 12132 downloads, 2 subscriptions

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

Changes:

The major new feature in this release is a Python API for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. Additionally, this release also adds simpler interfaces for learning to solve assignment and multi-target tracking problems.


Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9572 views, 3931 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19512 views, 3416 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo OpenOpt 0.53

by Dmitrey - March 15, 2014, 13:37:23 CET [ Project Homepage BibTeX Download ] 36652 views, 7729 downloads, 3 subscriptions

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About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE, MOP etc; general logical constraints, categorical variables, automatic differentiation, stochastic programming, interval analysis, many other goodies

Changes:

http://openopt.org/Changelog


Logo JMLR SHOGUN 3.2.0

by sonne - February 17, 2014, 20:31:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 51667 views, 10627 downloads, 5 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This is mostly a bugfix release:

Features

  • Fully support python3 now
  • Add mini-batch k-means [Parijat Mazumdar]
  • Add k-means++ [Parijat Mazumdar]
  • Add sub-sequence string kernel [lambday]

Bugfixes

  • Compile fixes for upcoming swig3.0
  • Speedup for gaussian process' apply()
  • Improve unit / integration test checks
  • libbmrm uninitialized memory reads
  • libocas uninitialized memory reads
  • Octave 3.8 compile fixes [Orion Poplawski]
  • Fix java modular compile error [Bjoern Esser]

About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

compilation problems fixed


Logo JMLR Waffles 2013-12-09

by mgashler - December 9, 2013, 18:04:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20368 views, 6345 downloads, 1 subscription

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Changed the license from LGPL to CC0. Added classes for stackable autoencoders and restricted boltzmann machines. Polished up the GBayesianNetwork class and add examples and unit tests. Added support for CMake. Made the build process also support clang, and be more mac-friendly. Simplified some important classes, including GMatrix and GNeuralNet. Enforced const correctness in more places. Nixed most uses of smart pointers. Made all learning algorithms thread-safe. Added thread-parallelism to several ensemble methods. Added support for binary division trees. Added some common activation functions. Added a tool to generate a vector of meta statistics about a dataset. Added several small-but-useful tools. Simplified the docs and web site.


Logo Theano 0.6

by jaberg - December 3, 2013, 20:32:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11277 views, 2131 downloads, 1 subscription

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.6 (December 3th, 2013)

Highlight:

* Last release with support for Python 2.4 and 2.5.
* We will try to release more frequently.
* Fix crash/installation problems.
* Use less memory for conv3d2d.

0.6rc4 skipped for a technical reason.

Highlights (since 0.6rc3):

* Python 3.3 compatibility with buildbot test for it.
* Full advanced indexing support.
* Better Windows 64 bit support.
* New profiler.
* Better error messages that help debugging.
* Better support for newer NumPy versions (remove useless warning/crash).
* Faster optimization/compilation for big graph.
* Move in Theano the Conv3d2d implementation.
* Better SymPy/Theano bridge: Make an Theano op from SymPy expression and use SymPy c code generator.
* Bug fixes.

Too much changes in 0.6rc1, 0.6rc2 and 0.6rc3 to list here. See https://github.com/Theano/Theano/blob/master/NEWS.txt for details.


Logo hca 0.41

by wbuntine - November 29, 2013, 03:16:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1551 views, 253 downloads, 2 subscriptions

About: Non-parametric topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Added example on using burstiness.


Logo GBAC 0.0.4

by henrydcl - November 22, 2013, 20:04:16 CET [ BibTeX BibTeX for corresponding Paper Download ] 1567 views, 552 downloads, 2 subscriptions

About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy

Changes:

Added bibtex information.


Logo JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12479 views, 3992 downloads, 1 subscription

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Logo bob 1.2.2

by anjos - October 28, 2013, 14:37:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4050 views, 791 downloads, 1 subscription

About: Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland.

Changes:

Bob 1.2.0 comes about 1 year after we released Bob 1.0.0. This new release comes with a big set of new features and lots of changes under the hood to make your experiments run even smoother. Some statistics:

Diff URL: https://github.com/idiap/bob/compare/v1.1.4...HEAD Commits: 629 Files changed: 954 Contributors: 7

Here is a quick list of things you should pay attention for while integrating your satellite packages against Bob 1.2.x:

  • The LBP module had its API changed look at the online docs for more details
  • LLRTrainer has been renamed to CGLogRegTrainer
  • The order in which you pass data to CGLogRegTrainer has been inverted (negatives now go first)
  • For C++ bindings, includes are in bob/python instead of bob/core/python
  • All specialized Bob exceptions are gone, if you were catching them, most have been cast into std::runtime_error's

For a detailed list of changes and additions, please look at our Changelog page for this release and minor updates:

https://github.com/idiap/bob/wiki/Changelog-from-1.1.4-to-1.2 https://github.com/idiap/bob/wiki/Changelog-from-1.2.0-to-1.2.1 https://github.com/idiap/bob/wiki/Changelog-from-1.2.1-to-1.2.2


Logo JMLR scikitlearn 0.14.1

by fabianp - October 4, 2013, 15:01:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10690 views, 3731 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 JMLR MSVMpack 1.4

by lauerfab - August 30, 2013, 10:40:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8911 views, 3229 downloads, 1 subscription

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Added parallelized k-Fold cross validation ('-cv k' option in trainmsvm)
  • Cache size now defaults to the maximum amount of memory
  • Minor bug fixes for the Matlab interface

Logo WEKA 3.7.10

by mhall - July 31, 2013, 05:27:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35482 views, 5055 downloads, 2 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

http://sourceforge.net/projects/weka/files/weka-3-7/3.7.10/README-3-7-10.txt/view


Logo JMLR Jstacs 2.1

by keili - June 3, 2013, 07:32:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12452 views, 2881 downloads, 2 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes:

  • MultipleIterationsCondition: Requires another TerminationCondition to fail a contiguous, specified number of times
  • ClassifierFactory: Allows for creating standard classifiers
  • SeqLogoPlotter: Plot PNG sequence logos from within Jstacs
  • MultivariateGaussianEmission: Multivariate Gaussian emission density for a Hidden Markov Model
  • MEManager: Maximum entropy model

New features and improvements:

  • Alignment: Added free shift alignment
  • PerformanceMeasure and sub-classes: Extension to weighted test data
  • AbstractClassifier, ClassifierAssessment and sub-classes: Adaption to weighted PerformanceMeasures
  • DNAAlphabet: Parser speed-up
  • PFMComparator: Extension to PFM from other sources/databases
  • ToolBox: New convenience methods for computing several statistics (e.g., median, correlation)
  • SignificantMotifOccurrencesFinder: New methods for computing PWMs and statistics from predictions
  • SequenceScore and sub-classes: New method toString(NumberFormat)
  • DataSet: Adaption to weighted data, e.g., partitioning
  • REnvironment: Changed several methods from String to CharSequence

Restructuring:

  • changed MultiDimensionalSequenceWrapperDiffSM to MultiDimensionalSequenceWrapperDiffSS

Several minor new features, bug fixes, and code cleanups


Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 16056 views, 3827 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 JMLR libDAI 0.3.1

by jorism - September 17, 2012, 14:17:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30810 views, 5786 downloads, 2 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).


Logo MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 44889 views, 8586 downloads, 2 subscriptions

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About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

Changes:

New features:

  • LibSvm(): pred_probability() now returns probability estimates; pred_values() added
  • LibLinear(): pred_values() and pred_probability() added
  • dtw_std: squared Euclidean option added
  • LCS for series composed by real values (lcs_real()) added
  • Documentation

Fix:

  • wavelet submodule: cwt(): it returned only real values in morlet and poul
  • IRelief(): remove np. in learn()
  • fix rfe_kfda and rfe_w2 when p=1

Showing Items 1-20 of 74 on page 1 of 4: 1 2 3 4 Next