Bounds for Learning from Evolutionary-Related Data in the Realizable Case
Résumé
This paper deals with the generalization ability of classifiers trained from non-iid evolutionary-related data in which all training and testing examples correspond to leaves of a phylogenetic tree. For the re-alizable case, we prove PAC-type upper and lower bounds based on symmetries and matchings in such trees.
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