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Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.clfs.sg.svm:
Wrap the libsvm package into a very simple class interface.
Bases: mvpa.clfs._svmbase._SVM
Support Vector Machine Classifier(s) based on Shogun
This is a simple base interface
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
_SVM
Interface class to Shogun’s classifiers and regressions.
Default implementation is ‘libsvm’.
SVM/SVR definition is dependent on specifying kernel, implementation type, and parameters for each of them which vary depending on the choices made.
Desired implementation is specified in svm_impl argument. Here is the list if implementations known to this class, along with specific to them parameters (described below among the rest of parameters), and what tasks it is capable to deal with (e.g. regression, binary and/or multiclass classification).
Implementations : | |
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Kernel choice is specified as a string argument kernel_type and it can be specialized with additional arguments to this constructor function. Some kernels might allow computation of per feature sensitivity.
Kernels : | |
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Parameters: |
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Access to the SVM model.
Dataset which was used for training
TODO – might better become state variable I guess