On the Learnability of Physically Unclonable Functions by Fatemeh Ganji

Author:Fatemeh Ganji
Language: eng
Format: epub, pdf
Publisher: Springer International Publishing, Cham

From this inequality, it can be inferred that the expected rate of disagreement is at least for the ideal hypothesis . Therefore, the separation factor of at least should be between an ideal hypothesis and an approximation of that cf. [6]. As stated in the following theorem, the maximum number of mistakes that can be made by the Perceptron algorithm is polynomial in this separation.

Theorem 4.4.1

Consider r labeled examples which are fed into the Perceptron algorithm, and let . In the case of noisy labels, let be the solution vector with , and . Then

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