A Method of Sieves for Estimation and Classiication of Gaussian Data
نویسندگان
چکیده
Many classiication problems use image or other high-dimensional data, and must be designed from training data. The design and analysis of such systems param-eterized by unknown functions, based on a method of sieves to regularize the function estimates, is described. The test statistic is assumed to be the ideal test statistic with estimated functions substituted for the truth. The test statistic is decomposed into approximation error and estimation error components, providing analytical tools for determining the optimal sieve size.
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