Additional functionality for Support Vector Machines in TMVA #125
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…nal kernel functions for svms and add additional functionality to svm method.
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Additional functionality for SVMs includes:
~ Multi-gaussian, product and sum kernel functions added, as well as polynomial kernel function re-enabled.
~ Parameter optimisation for kernel parameters and cost added following the implementation for BDT parameter optimisation.
~ Can specify the parameters to be optimised and the range over which they are optimised.
~ Calculation of loss functions, though not currently used.
~ Weighting of the cost parameter to the relative signal and background dataset sizes, as to not bias the SVM training if one dataset is significantly larger than the other.
~ Also return of map of optimised parameters from the tmva factory to allow for use in external program.