By Johan A. K. Suykens
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In addition PROOF. Let
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Roughly speaking, the "bias" of a solution / coincides with our approximation error, and its "variance" with the sample error. Quoting , A model which is too simple, or too inflexible, will have a large bias, while one which has too much flexibility in relation to the particular data set will have a large variance. e. the smallest error] is obtained when we have the best compromise between the conflicting requirements of small bias and small variance. 42 F. Cucker, S. Smale As described in Section 3, Chapter II in [CS], the bias-variance problem amounts to the choice of a compact subspace H of ^(X) over which £z is minimized.