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Logo OpenOpt 0.54

by Dmitrey - June 15, 2014, 14:50:37 CET [ Project Homepage BibTeX Download ] 82393 views, 16711 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 Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16697 views, 4709 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


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 ] 22613 views, 7609 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 ] 50790 views, 7888 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).


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 GBAC 0.0.4

by henrydcl - November 22, 2013, 20:04:16 CET [ BibTeX BibTeX for corresponding Paper Download ] 8034 views, 2216 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 ] 28245 views, 8674 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 ] 14138 views, 2762 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 MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 35654 views, 7494 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 MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 88259 views, 15743 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

Logo JMLR LWPR 1.2.4

by sklanke - February 6, 2012, 19:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41155 views, 4814 downloads, 1 subscription

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]

Changes:

Version 1.2.4

  • Corrected typo in lwpr.c (wrong function name for multi-threaded helper function on Unix systems) Thanks to Jose Luis Rivero

Logo BCILAB 1.0-beta

by chkothe - January 6, 2012, 23:47:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7073 views, 1520 downloads, 1 subscription

About: MATLAB toolbox for advanced Brain-Computer Interface (BCI) research.

Changes:

Initial Announcement on mloss.org.


Logo SGD 2.0

by leonbottou - October 11, 2011, 20:59:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16736 views, 2599 downloads, 5 subscriptions

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About: The SGD-2.0 package contains implementations of the SGD and ASGD algorithms for linear SVMs and linear CRFs.

Changes:

Version 2.0 features ASGD.


About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search.

Changes:

See project page for changes.


Logo JMLR FastInf 1.0

by arielj - June 4, 2010, 14:04:37 CET [ Project Homepage BibTeX Download ] 12391 views, 4483 downloads, 1 subscription

About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm.

Changes:

Initial Announcement on mloss.org.


Logo asp 0.3

by sonne - May 7, 2010, 10:25:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12473 views, 2374 downloads, 1 subscription

About: Accurate splice site predictor for a variety of genomes.

Changes:

Asp now supports three formats:

-g fname for gff format

-s fname for spf format

-b dir for a binary format compatible with mGene.

And a new switch

-t which switches on a sigmoid-based transformation of the svm scores to get scores between 0 and 1.


About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others.

Changes:
  • Modified specification of optional parameters (using sfo_opt)
  • Added sfo_ls_lazy for maximizing nonnegative submodular functions
  • Added sfo_fn_infogain, sfo_fn_lincomb, sfo_fn_invert, ...
  • Added additional documentation and more examples
  • Now Octave ready

About: A Java library to create, process and manage mixtures of exponential families.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Shark 2.3.0

by igel - October 24, 2009, 22:12:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36120 views, 6741 downloads, 1 subscription

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About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques.

Changes:
  • moved to GitHub
  • new build system
  • minor bug fixes

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 25705 views, 9093 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.


Showing Items 21-40 of 82 on page 2 of 5: Previous 1 2 3 4 5 Next