Meta learning of bounds on the Bayes classifier error

27 Apr 2015Kevin R. MoonVeronique DelouilleAlfred O. Hero III

Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier... (read more)

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