SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
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This major update adds many improvements, new features, and bugfixes. It includes everything which has been carried out before and during the Google Summer of Code 2012. Students have implemented various new features such as structured output learning, gaussian processes, latent variable SVM (and structured output learning), statistical tests in kernel reproducing spaces, various multitask learning algorithms, and various usability improvements, to name a few.
This release introduced the concept of 'converters', which enables you to construct embeddings of arbitrary features. It also includes a few new dimension reduction techniques and significant performance improvements in the dimensionality reduction toolkit. Other improvements include a significant compilation speed-up, various bugfixes for modular interfaces and algorithms, and improved Cygwin, Mac OS X, and clang++ compatibility. Github Issues is now used for tracking bugs and issues.
This release features interfaces to
new languages including Java, C#, Ruby, and Lua, a model selection framework, many dimension reduction techniques, Gaussian Mixture Model estimation, and a full-fledged online learning framework.
This is a major new release with lots of internal but also user visible changes. First of all, it now includes a number of applications (in the applications folder) and all the data sets are now contained in a separate tarball. For the user, the most interesting and important feature is serialization support. One can now dump any shogun object to disk and load it later on. Supported serialization formats include .hdf5, ascii, .json, XML formats, and Python pickle version 1 and 2.
This release contains several enhancements, cleanups, and bugfixes. A number of new string kernels and multi-class MKL were implemented. Support for python-dbg was added. Floats are now accepted as input for custom kernels that now can be more than 4GB in size. Python installation uses distutils now. Static linking has been fixed, as well as the sparse linear kernels add_to_normal function.