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Logo JMLR dlib ml 18.1

by davis685 - March 25, 2013, 23:48:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 53358 views, 9342 downloads, 1 subscription

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:

In addition to some bug fixes, this release also brings the following notable improvements to the library:

  • The SURF feature extraction tool has higher matching accuracy than in previous dlib releases.
  • The cutting plane optimizer is now faster
  • A new tool for computing the singular value decomposition of very large matrices
  • A new tool for performing canonical correlation analysis on large datasets
  • A new tool for easily writing parallel for loops

Logo JMLR SHOGUN 2.1.0

by sonne - March 17, 2013, 13:59:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41072 views, 8602 downloads, 4 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 release also contains several enhancements, cleanups and bugfixes:

Features

  • Linear Time MMD two-sample test now works on streaming-features, which allows to perform tests on infinite amounts of data. A block size may be specified for fast processing. The below features were also added. By Heiko Strathmann.
  • It is now possible to ask streaming features to produce an instance of streamed features that are stored in memory and returned as a CFeatures* object of corresponding type. See CStreamingFeatures::get_streamed_features().
  • New concept of artificial data generator classes: Based on streaming features. First implemented instances are CMeanShiftDataGenerator and CGaussianBlobsDataGenerator. Use above new concepts to get non-streaming data if desired.
  • Accelerated projected gradient multiclass logistic regression classifier by Sergey Lisitsyn.
  • New CCSOSVM based structured output solver by Viktor Gal
  • A collection of kernel selection methods for MMD-based kernel two- sample tests, including optimal kernel choice for single and combined kernels for the linear time MMD. This finishes the kernel MMD framework and also comes with new, more illustrative examples and tests. By Heiko Strathmann.
  • Alpha version of Perl modular interface developed by Christian Montanari.
  • New framework for unit-tests based on googletest and googlemock by Viktor Gal. A (growing) number of unit-tests from now on ensures basic funcionality of our framework. Since the examples do not have to take this role anymore, they should become more ilustrative in the future.
  • Changed the core of dimension reduction algorithms to the Tapkee library.

Bugfixes

  • Fix for shallow copy of gaussian kernel by Matt Aasted.
  • Fixed a bug when using StringFeatures along with kernel machines in cross-validation which cause an assertion error. Thanks to Eric (yoo)!
  • Fix for 3-class case training of MulticlassLibSVM reported by Arya Iranmehr that was suggested by Oksana Bayda.
  • Fix for wrong Spectrum mismatch RBF construction in static interfaces reported by Nona Kermani.
  • Fix for wrong include in SGMatrix causing build fail on Mac OS X (thanks to @bianjiang).
  • Fixed a bug that caused kernel machines to return non-sense when using custom kernel matrices with subsets attached to them.
  • Fix for parameter dictionary creationg causing dereferencing null pointers with gaussian processes parameter selection.
  • Fixed a bug in exact GP regression that caused wrong results.
  • Fixed a bug in exact GP regression that produced memory errors/crashes.
  • Fix for a bug with static interfaces causing all outputs to be -1/+1 instead of real scores (reported by Kamikawa Masahisa).

Cleanup and API Changes

  • SGStringList is now based on SGReferencedData.
  • "confidences" in context of CLabel and subclasses are now "values".
  • CLinearTimeMMD constructor changes, only streaming features allowed.
  • CDataGenerator will soon be removed and replaced by new streaming- based classes.
  • SGVector, SGMatrix, SGSparseVector, SGSparseVector, SGSparseMatrix refactoring: Now contains load/save routines, relevant functions from CMath, and implementations went to .cpp file.

Logo r-cran-caret 5.15-61

by r-cran-robot - February 12, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 34165 views, 7417 downloads, 1 subscription

About: Classification and Regression Training

Changes:

Fetched by r-cran-robot on 2013-05-01 00:00:05.351338


Logo MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 38050 views, 7218 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 Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 13053 views, 6459 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 OpenOpt 0.45

by Dmitrey - March 15, 2013, 14:27:12 CET [ Project Homepage BibTeX Download ] 29138 views, 6207 downloads, 1 subscription

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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, interval analysis, many other goodies

Changes:

http://openopt.org/Changelog


Logo Armadillo library 3.810

by cu24gjf - April 22, 2013, 05:24:18 CET [ Project Homepage BibTeX Download ] 27194 views, 6179 downloads, 2 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL).

Changes:
  • added fast Fourier transform
  • added handling of .imbue() and .transform() by submatrices and subcubes
  • added batch insertion constructors for sparse matrices
  • minor fix for multiplication of complex sparse matrices
  • better detection of recent Intel MKL versions during installation

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

Changes:

Markov decision processes added (Kan & Shelton 2008) [ctmdp.h]

Mean field inference added (Cohn, El-Hay, Friedman, & Kupferman 2009) [meanfieldinf.h]

Factored uniformization for filtering added (Celikkaya & Shelton 2011) [uniformizedfactoredinf.h]

Auxilliary Gibbs sampling added (Rao & Teh 2011) [gibbsauxsampler.h]

Multi-threading for EM added

many speed improvements

unit testing improved [tst/]

new demo "main" programs added [demo/]

file format changed to XML-ish format (with old methods still there for conversion)

matrix switched to Eigen package (with option to return to old matrix)

glpk now included

initial cmake functionality


Logo JMLR Waffles 2013-04-06

by mgashler - April 7, 2013, 02:04:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16138 views, 5316 downloads, 1 subscription

About: A broad collection of script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a C++ class library.)

Changes:

See the change log at http://waffles.sourceforge.net/changelog.html


Logo r-cran-earth 3.2-6

by r-cran-robot - May 1, 2013, 00:00:06 CET [ Project Homepage BibTeX Download ] 25207 views, 5172 downloads, 1 subscription

About: Multivariate Adaptive Regression Spline Models

Changes:

Fetched by r-cran-robot on 2013-05-01 00:00:06.060849


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