classyfire: Robust multivariate classification using highly optimised SVM ensembles

A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.

AuthorEleni Chatzimichali <> and Conrad Bessant <>
Date of publication2015-01-12 01:08:41
MaintainerEleni Chatzimichali <>
LicenseGPL (>= 2)

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