A Bayesian CMAC for High Assurance Learning
نویسندگان
چکیده
We analyze the drawbacks to using ANNs in high assurance systems and propose a solution based upon a Bayesian approach with a specific network topology that can be solved in closed form. The Bayesian approach leads to better answers in the traditional sense, while also allowing us to quantify risk and deal with it in a reasonable manner. We demonstrate this approach on several synthetic functions and the Abalone data set.
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تاریخ انتشار 2007