Derive general expression for generalization error (GE) of k-nearest neighbors learning approach where GE is defined...
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Derive general expression for generalization error (GE) of k-nearest neighbors learning approach where GE is defined as: GE(k)= E[(Y-R,)*] In the k-nearest neighbors learning approach output, Ÿ, is defined as: Y: =/(x)=1/ Σ e{i eN (x)} where N(x) is a set of k nearest neighbors of x within the observed (training) data. where: The observed noisy data can be described as: I Yiraning = f(x)+ E, i= 1,.., Ntraning = E[e] =0 E= traning var(E)= 1 (2) = £[(8- €) ²] = 0² E[ee] =0, if izj Derive general expression for generalization error (GE) of k-nearest neighbors learning approach where GE is defined as: GE(k)= E[(Y-R,)*] In the k-nearest neighbors learning approach output, Ÿ, is defined as: Y: =/(x)=1/ Σ e{i eN (x)} where N(x) is a set of k nearest neighbors of x within the observed (training) data. where: The observed noisy data can be described as: I Yiraning = f(x)+ E, i= 1,.., Ntraning = E[e] =0 E= traning var(E)= 1 (2) = £[(8- €) ²] = 0² E[ee] =0, if izj
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We want to calculate the expected generalization error GE for the knearest neighbors learning approach To do this we will first find the conditional expectation of Y given X which we will call RX Sinc... View the full answer
Related Book For
Smith and Roberson Business Law
ISBN: 978-0538473637
15th Edition
Authors: Richard A. Mann, Barry S. Roberts
Posted Date:
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