Extending Occam's Razor
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چکیده
Occam's Razor states that, all other things being equal, the simpler of two possible hypotheses is to be preferred. A quanti ed version of Occam's Razor has been proven for the PAC model of learning, giving sample-complexity bounds for learning using what Blumer et al. call an Occam algorithm [1]. We prove an analog of this result for Haussler's more general learning model, which encompasses learning in stochastic situations, learning real-valued functions, etc.
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تاریخ انتشار 1994