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- W2140991775 abstract "The present work investigates Gold style algorithmic learning from input-output examples whereby the learner has access to oracles as additional information. Furthermore this access has to be robust, that means that a single learning algorithm has to succeed with every oracle which meets a given specification. The first main result considers oracles of the same Turing degree: Robust learning with any oracle from a given degree does not achieve more than learning without any additional information.The further work considers learning from function oracles which describe the whole class of functions to be learned in one of the following four ways: the oracle is a list of all functions in this class or a predictor for this class or a one-sided classifier accepting just the functions in this class or a martingale succeeding on this class.It is shown that for learning in the limit (Ex), lists are the most powerful additional information, the powers of predictors and classifiers are incomparable and martingales are of no help at all. Similar results are obtained for the criteria of predicting the next value, finite, Popperian and finite Popperian learning. Lists are omniscient for the criterion of predicting the next value but some classes can not be Ex-learned with any of these types of additional information. The class REC of all recursive functions is Ex-learnable with the help of a list, a predictor or a classifier." @default.
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- W2140991775 date "1997-01-01" @default.
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- W2140991775 title "Robust learning with infinite additional information" @default.
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- W2140991775 doi "https://doi.org/10.1007/3-540-62685-9_26" @default.
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