Neural Comput - Determination and the no-free-lunch paradox.

Tópicos

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Resumo

We discuss the no-free-lunch NFL theorem for supervised learning as a logical paradox--that is, as a counterintuitive result that is correctly proven from apparently incontestable assumptions. We show that the uniform prior that is used in the proof of the theorem has a number of unpalatable consequences besides the NFL theorem, and propose a simple definition of determination (by a learning set of given size) that casts additional suspicion on the utility of this assumption for the prior. Whereas others have suggested that the assumptions of the NFL theorem are not practically realistic, we show these assumptions to be at odds with supervised learning in principle. This analysis suggests a route toward the establishment of a more realistic prior probability for use in the extended Bayesian framework.

Resumo Limpo

discuss nofreelunch nfl theorem supervis learn logic paradoxthat counterintuit result correct proven appar incontest assumpt show uniform prior use proof theorem number unpalat consequ besid nfl theorem propos simpl definit determin learn set given size cast addit suspicion util assumpt prior wherea other suggest assumpt nfl theorem practic realist show assumpt odd supervis learn principl analysi suggest rout toward establish realist prior probabl use extend bayesian framework

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