Sept. 1, 2012 - Soeren Sonnenburg -

SHOGUN Release version 2.0.0 (libshogun 12.0, data 0.4, parameter 1)
This release also contains several enhancements, cleanups and bugfixes:

  • Features:
    • This release contains first release of Efficient Dimensionality Reduction Toolkit (EDRT).
    • Support for new SWIG -builtin python interface feature (SWIG 2.0.4 is required now).
    • EDRT algorithms are now available using static interfaces such as matlab and octave.
    • Jensen-Shannon kernel and Homogeneous kernel map preprocessor (thanks to Viktor Gal).
    • New 'multiclass' module for multiclass classification algorithms, generic linear and kernel multiclass machines, multiclass LibLinear and OCAS wrappers, new rejection schemes concept by Sergey Lisitsyn.
    • Various multitask learning algorithms including L1/Lq multitask group lasso logistic regression and least squares regression, L1/L2 multitask tree guided group lasso logistic regression and least squares regression, trace norm regularized multitask logistic regression, clustered multitask logistic regression and L1/L2 multitask group logistic regression by Sergey Lisitsyn.
    • Group and tree-guided logistic regression for binary and multiclass problems by Sergey Lisitsyn.
    • Mahalanobis distance, QDA, Stochastic Proximity Embedding, generic OvO multiclass machine and CoverTree and KNN integation (thanks to Fernando J. Iglesias Garcia).
    • Structured output learning framework by Fernando J. Iglesias Garcia.
    • Hidden markov support vector machine structured output model by Fernando J. Iglesias Garcia.
    • Implementations of three Bundle method for risk minimization (BMRM) variants by Michal Uricar.
    • Latent SVM framework and latent detector example by Viktor Gal.
    • Gaussian processes framework for parameters selection and gaussian processes regression estimation framework by Jacob Walker.
    • New graphical python modular examples.
    • Standard Cross-Validation splitting for regression problems by Heiko Strathmann
    • New data-locking concept by Heiko Strathmann which allows to tell machines that data is not going to change during training/testing until unlocked. KernelMachines now make use of that by not recomputing kernel matrix in cross-validation.
    • Cross-validation for KernelMachines is now parallelized.
    • Cross-validation is now possible with custom kernels.
    • Features may now have arbritarily many index subsets (of subsets (of subsets (...))).
    • Various clustering measures, Least Angle Regression and new multiclass strategies concept (thanks to Chiyuan Zhang).
    • A bunch of multiclass learning algorithms including the ShareBoost algorithm, ECOC framework, conditional probability tree, balanced conditional probability tree, random conditional probability tree and relaxed tree by Chiyuan Zhang.
    • Python Sparse matrix typemap for octave modular interface (thanks to Evgeniy Andreev).
    • Newton SVM port (thanks to Harshit Syal).
    • Some progress on native windows compilation using cmake and mingw-w64 (thanks to Josh aka jklontz).
    • CMake compilation improvements (thanks to Eric aka yoo).
  • Bugfixes:
    • Fix for bug in the Gaussian Naive Bayes classifier, its domain was changed to log-space.
    • Fix for R_static interface installation (thanks Steve Lianoglou).
    • SVMOcas memsetting and max_train_time bugfix.
    • Various fixes for compile errors with clang.
    • Stratified-cross-validation now used different indices for each run.
  • Cleanup and API Changes:
    • Various code cleanups by Evan Shelhamer
    • Parameter migration framework by Heiko Strathmann. From now on, changes in the shogun objects will not break loading old serialized files anymore

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