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  1. The meta-inductive justification of induction.Tom F. Sterkenburg - 2020 - Episteme 17 (4):519-541.
    I evaluate Schurz's proposed meta-inductive justification of induction, a refinement of Reichenbach's pragmatic justification that rests on results from the machine learning branch of prediction with expert advice. My conclusion is that the argument, suitably explicated, comes remarkably close to its grand aim: an actual justification of induction. This finding, however, is subject to two main qualifications, and still disregards one important challenge. The first qualification concerns the empirical success of induction. Even though, I argue, Schurz's argument does not need (...)
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  • (2 other versions)A Treatise of Human Nature.David Hume & A. D. Lindsay - 1958 - Philosophical Quarterly 8 (33):379-380.
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  • No Free Lunch Theorems for Optimization.D. H. Wolpert & W. G. Macready - 1997 - IEEE Transactions on Evolutionary Computation 1 (1):67–82.
    A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization (...)
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  • The Lack of A Priori Distinctions Between Learning Algorithms.David H. Wolpert - 1996 - Neural Computation 8 (7):1341–1390.
    This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower (...)
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