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- W2014089785 abstract "We put Bayes decision theory into the framework of pac-learning as introduced by Valiant [Val84]. Unlike classical Boolean concept learning where functions f : {0, l} n → {0,1} are approximated, we assume here that f (x) is 0 (or 1) with a certain probability. We develop a theoretical framework for estimating functions and reduce the classification problem to the problem of estimating parameters. Within this framework it is shown that classifications based on n conditional independent Boolean features can efficiently be learned by examples. Our learning algorithm achieves with probability 1 — δ an error which comes arbitrarily close (up to an additive e) to the optimal one of a perfect Bayes decision. It requires examples. In the particular case of two state classification, learning can be performed on a single neuron. Moreover we relax the restriction of conditional independence to dependencies of bounded order k and show that in this case we need examples." @default.
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- W2014089785 date "1991-08-15" @default.
- W2014089785 modified "2023-09-26" @default.
- W2014089785 title "Probably almost Bayes decisions" @default.
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- W2014089785 doi "https://doi.org/10.5555/114836.114845" @default.
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