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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 17423 views, 7476 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 python weka wrapper 0.1.17

by fracpete - December 17, 2014, 21:43:28 CET [ Project Homepage BibTeX Download ] 6763 views, 1419 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.

Changes:
  • fixed "setup.py" to download Weka 3.7.12 instead of 3.7.11 (this time correct URL)

Logo Accord.NET Framework 2.14.0

by cesarsouza - December 9, 2014, 23:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16937 views, 3439 downloads, 2 subscriptions

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.

Changes:

Adding a large number of new distributions, such as Anderson-Daring, Shapiro-Wilk, Inverse Chi-Square, Lévy, Folded Normal, Shifted Log-Logistic, Kumaraswamy, Trapezoidal, U-quadratic and BetaPrime distributions, Birnbaum-Saunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions.

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

https://github.com/accord-net/framework/releases


Logo libAGF 0.9.8

by Petey - December 6, 2014, 02:35:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9254 views, 1849 downloads, 2 subscriptions

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.8:

  • bug fixes: svm file conversion works properly and is more general

  • non-hierarchical multi-borders has 3 options for solving for the conditional probabilities: matrix inversion, voting, and matrix inversion over-ridden by voting, with re-normalization

  • multi-borders now works with external binary classifiers

  • random numbers resolve a tie when selecting classes based on probabilities

  • pair of routines, sort_discrete_vectors and search_discrete_vectors, for classification based on n-d binning (still experimental)

  • command options have been changed with many new additions, see QUICKSTART file or run the relevant commands for details


Logo The Statistical ToolKit 0.8.4

by joblion - December 5, 2014, 13:21:47 CET [ Project Homepage BibTeX Download ] 661 views, 190 downloads, 2 subscriptions

About: STK++: A Statistical Toolkit Framework in C++

Changes:

Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix


About: a parallel LDA learning toolbox in Multi-Core Systems for big topic modeling.

Changes:

Initial Announcement on mloss.org.


Logo KeBABS 1.0.2

by UBod - December 4, 2014, 09:15:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1008 views, 147 downloads, 1 subscription

About: Kernel-Based Analysis Of Biological Sequences

Changes:
  • a few C code changes for mismatch kernel
  • correction of MCC
  • correction of computation of feature weights for LiblineaR with more than 3 classes

Logo BayesPy 0.2.3

by jluttine - December 3, 2014, 14:51:10 CET [ Project Homepage BibTeX Download ] 2773 views, 750 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Fix matplotlib compatibility broken by recent changes in matplotlib>=1.4.0
  • Add random sampling for Binomial and Bernoulli nodes
  • Fix minor bugs, for instance, in plot module

Logo Optunity 1.0.1

by claesenm - December 2, 2014, 15:11:47 CET [ Project Homepage BibTeX Download ] 1021 views, 283 downloads, 1 subscription

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.

Changes:

Bugfixes related to Python 3. Added smoke tests for all solvers to prevent similar issues in the future.


Logo pyGPs 1.3.1

by mn - December 1, 2014, 17:36:32 CET [ Project Homepage BibTeX Download ] 3010 views, 721 downloads, 3 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.3.1

November 25th 2014

structural updates:

  • full inline documentation with input parameter and output specified

  • check for the inputs and provide diagnostic messages for some inputs

  • consistant naming in inline and online documentation

  • string representation for dnlZStruct and postStruct. Now you can do sth like:

nlZ, dnlZ, post = model.getPosterior(x,y)

print post

  • instead of a python object, we provide now a more informative description.

  • add optimization into unit test routines. Also add checking for cholesky decomposition and checking positive-definite property of kernel matrix.

  • add jitter to the digonal of linear, linARD, and poly covariance for numerical stability.

  • fix several minor problems in unit test framework

  • hierachically rearranged for online documentation

  • add several supplementary instruction in online documentation


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