Sept. 1, 2012 - Soeren Sonnenburg - email@example.com
SHOGUN Release version 2.0.0 (libshogun 12.0,
data 0.4, parameter 1)
This release also contains several enhancements, cleanups and bugfixes:
- 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).
- 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