Multivariate Pattern Analysis in Python

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# mvpa2.clfs.svm

Importer for the available SVM and SVR machines.

Multiple external libraries implementing Support Vector Machines (Classification) and Regressions are available: LIBSVM, and shogun. This module is just a helper to provide default implementation for SVM depending on the availability of external libraries. By default LIBSVM implementation is chosen by default, but in any case both libraries are available through importing from this module:

    > from mvpa2.clfs.svm import sg, libsvm
    > help(sg.SVM)
    > help(libsvm.SVM)

Please refer to particular interface for more documentation about parameterization and available kernels and implementations.

Classes

`LinearCSVMC`([C]) | C-SVM classifier using linear kernel.
`LinearNuSVMC`([nu]) | Nu-SVM classifier using linear kernel.
`LinearSVMKernel` | A simple Linear kernel: K(a,b) = a*b.T ..
`RbfCSVMC`([C]) | C-SVM classifier using a radial basis function kernel See documentation of `AttributesCollector` for more information ..
`RbfNuSVMC`([nu]) | Nu-SVM classifier using a radial basis function kernel See documentation of `AttributesCollector` for more information ..
`RbfSVMKernel` | Radial Basis Function kernel (aka Gaussian): K(a,b) = exp(-gamma*||a-b||**2) ..
`SVM`(**kwargs) | Support Vector Machine Classifier.

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