Name : shogun Relocations: (not relocatable) Version : 3.2.0 Vendor: ALT Linux Team Release : alt1 Build Date: Tue Jun 3 20:12:51 2014 Install date: (not installed) Build Host: real-sisyphus.hasher.altlinux.org Group : Sciences/Mathematics Source RPM: (none) Size : 22491035 License: GPL v3 or later Packager : Eugeny A. Rostovtsev (REAL) <real at altlinux.org> URL : http://www.shogun-toolbox.org/ Summary : A Large Scale Machine Learning Toolbox Description : 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 and SVMlight. 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 Discriminant Analysis (LDA), Linear Programming Machine (LPM), (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.