The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing misclassification rates.
CITATION STYLE
Brenig, W. (1989). Fokker-Planck and Langevin Equations. In Statistical Theory of Heat (pp. 188–192). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-74685-7_38
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