Shogun A large scale machine learning toolbox
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Shogun Description

A large scale machine learning toolbox The machine learning toolbox's 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, among them the state of the art LibSVM, SVMLight, SVMLi and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the combined kernel which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Programming Machine (LPM), Linear Discriminant Analysis (LDA), (Kernel) Perceptrons and features algorithms to train hidden markov models.The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. What's New in This Release: · Replace ambigous self-defined data types for char/int/float etc. by 'standardized' types. · Method classify() in WDSVMOcas now has a default value for its argument. · Removed a few stderr debug outputs. · Testsuite now covers subSVMs in MultiClassSVMs, static interfaces now support commands GET_NUM_SVMS and GET_SVM for MultiClassSVMs. · Fix for contigous arrays/vectors in interface for Python modular. · Fixed improper assignment of labels in constructor of WDSVMOcas leading to segfaults on destruction in (python) modular interface. · Fixed a segfault opportunity in MultiClassSVM.

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