The average sensitivity of an intersection of half spaces

STOC(2014)

引用 23|浏览11
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摘要
Abstract We prove new bounds on the average sensitivity of the indicator function of an intersection of k halfspaces. In particular, we prove the optimal bound of . This generalizes a result of Nazarov, who proved the analogous result in the Gaussian case, and improves upon a result of Harsha, Klivans and Meka. Furthermore, our result has implications for the runtime required to learn intersections of halfspaces. AMS Subject Classification Primary; 52C45
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关键词
linear threshold function,general,learning theory,theory,noise sensitivity,learning
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