Oct. 28, 2013 - Soeren Sonnenburg - sonne@debian.org>

SHOGUN Release version 3.0.0 (libshogun 14.0, data 0.6, parameter 1)
This release features 8 successful Google Summer of Code projects and it is the result of an incredible effort by our students. All projects come with very cool ipython-notebooks that contain background, code examples and visualizations. These can be found on our webpage!

    The projects are:
  • Gaussian Processes for binary classification [Roman Votjakov]
  • Sampling log-determinants for large sparse matrices [Soumyajit De]
  • Metric Learning via LMNN [Fernando Iglesias]
  • Independent Component Analysis (ICA) [Kevin Hughes]
  • Hashing Feature Framework [Evangelos Anagnostopoulos]
  • Structured Output Learning [Hu Shell]
  • A web-demo framework [Liu Zhengyang] Other important changes are the change of our build-system to cmake and the addition of clone/equals methods to our base-class. In addition, you get the usual ton of bugfixes, new unit-tests, and new mini-features.
  • Features:
    • In addition, the following features have been added:
    • Added method to importance sample the (true) marginal likelihood of a Gaussian Process using a posterior approximation.
    • Added a new class for classical probability distribution that can be sampled and whose log-pdf can be evaluated. Added the multivariate Gaussian with various numerical flavours.
    • Cross-validation framework works now with Gaussian Processes
    • Added nu-SVR for LibSVR class
    • Modelselection is now supported for parameters of sub-kernels of combined kernels in the MKL context. Thanks to Evangelos Anagnostopoulos
    • Probability output for multi-class SVMs is now supported using various heuristics. Thanks to Shell Xu Hu.
    • Added an "equals" method to all Shogun objects that recursively compares all registered parameters with those of another instance -- up to a specified accuracy.
    • Added a "clone" method to all Shogun objects that creates a deep copy
    • Multiclass LDA. Thanks to Kevin Hughes.
    • Added a new datatype, complex128_t, for complex numbers. Math functions, support for SGVector/Matrix, SGSparseVector/Matrix, and serialization with Ascii and Xml files added. [Soumyajit De].
    • Added mini-framework for numerical integration in one variable. Implemented Gauss-Kronrod and Gauss-Hermite quadrature formulas.
    • Changed from configure script to CMake by Viktor Gal.
    • Add C++0x and C++11 cmake detection scripts
    • ND-Array typmap support for python and octave modular.
  • Bugfixes:
    • Fix json serialization.
    • Fixed bugs in FITC inference method that caused wrong posterior results.
    • Fixed bugs in GP Regression that caused negative values for the variances.
    • Fixed two memory errors in the streaming-features framework.
    • Fixed bug in the Kernel Mean Matching implementation (thanks to Meghana Kshirsagar).
  • Cleanup and API Changes:
    • Switch compile system to cmake
    • SGSparseVector/Matrix are now derived from SGReferenceData and thus refcounted.
    • Move README and INSTALL files to top level directory.
    • Use common RefCount class for ReferencedData and CSGObjects.
    • Rename HMSVMLabels to SequenceLabels
    • Refactored method to fit a sigmoid to SVM scores, now in CStatistics, still called from CBinaryLabels.
    • Use Dynamic arrays to hold preprocessors in features instead of raw pointers.
    • Use Dynamic arrays to hold Features in CombinedFeatures.
    • Use Dynamic arrays to hold Kernels in CombinedKernels/ProductKernels.
    • Use Eigen3 for GPs, LDA

What's New

Feb. 9, 2016 -> SHOGUN 4.1.0
Jan. 26, 2015 -> SHOGUN 4.0.0
Feb. 17, 2014 -> SHOGUN 3.2.0
Jan. 6, 2014 -> SHOGUN 3.1.1
Jan. 5, 2014 -> SHOGUN 3.1.0
Oct. 28, 2013 -> SHOGUN 3.0.0
March 17, 2013 -> SHOGUN 2.1.0