Description
The focus of ''Shogun'' is on kernel machines such as support vector machines for regression andSupported algorithms
Currently ''Shogun'' supports the following algorithms: * Support vector machines * Dimensionality reduction algorithms, such as PCA, Kernel PCA, Locally Linear Embedding, Hessian Locally Linear Embedding, Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Multidimensional Scaling, Isomap, Diffusion Maps, Laplacian Eigenmaps * Online learning algorithms such as SGD-QN, Vowpal Wabbit * Clustering algorithms: k-means and GMM * Kernel Ridge Regression, Support Vector Regression * Hidden Markov Models * K-Nearest Neighbors * Linear discriminant analysis * Kernel Perceptrons. Many different kernels are implemented, ranging from kernels for numerical data (such as gaussian or linear kernels) to kernels on special data (such as strings over certain alphabets). The currently implemented kernels for numeric data include: * linear * gaussian * polynomial * sigmoid kernels The supported kernels for special data include: * Spectrum * Weighted Degree * Weighted Degree with Shifts The latter group of kernels allows processing of arbitrary sequences over fixed alphabets such as DNA sequences as well as whole e-mail texts.Special features
As ''Shogun'' was developed with bioinformatics applications in mind it is capable of processing huge datasets consisting of up to 10 million samples. ''Shogun'' supports the use of pre-calculated kernels. It is also possible to use a combined kernel i.e. a kernel consisting of a linear combination of arbitrary kernels over different domains. The coefficients or weights of the linear combination can be learned as well. For this purpose ''Shogun'' offers a ''multiple kernel learning'' functionality.References
* S. Sonnenburg, G. Rätsch, S. Henschel, C. Widmer, J. Behr, A. Zien, F. De Bona, A. Binder, C. Gehl and V. Franc: ''The SHOGUN Machine Learning Toolbox'', Journal of Machine Learning Research, 11:1799−1802, June 11, 2010. * M. Gashler. Waffles: A Machine Learning Toolkit. Journal of Machine Learning Research, 12 (July):2383–2387, 2011. * P. Vincent, Y. Bengio, N. Chapados, and O. Delalleau. Plearn high-performance machine learning library. URL http://plearn.berlios.de/.External links