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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021
摘要
The important recent book by G. Schurz [1] appreciates that the no-free- lunch theorems (NFL) have major implications for the problem of (meta) induction. Here I review the NFL theorems, emphasizing that they do not only concern the case where there is a uniform prior — they prove that there are “as many priors” (loosely speaking) for which any induction algorithm A out-generalizes some induction algorithm B as vice-versa. Importantly though, in addition to the NFL theorems, there are many free lunch theorems. In particular, the NFL theorems can only be used to compare the marginal expected performance of an induction algorithm A with the marginal expected performance of an induction algorithm B. There is a rich set of free lunches which instead concern the 統計 correlations among the genera
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