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.

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

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.

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.

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

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

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.)

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