Estimating the Expected Error of Empirical Minimizers for Model Selection

Tobias Scheffer, Thorsten Joachims

Model selection is considered the problem of choosing a hypothesis language which provides an optimal balance between low empirical error and high structural complexity. In this Abstract, we discuss the intuition of a new, very efficient approach to model selection. Our approach is inherently Bayesian, but instead of using priors on target functions or hypotheses, we talk about priors on error values--which leads us to a new mathematical characterization of the expected true error.

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