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Selection: Wolpert:DH [4 articles] 

Publications by author Wolpert:DH.

Stacked generalization

Neural Networks, Vol. 5, No. 2. (January 1992), pp. 241-259,


This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When ...


The lack of a priori distinctions between learning algorithms

Neural Computation, Vol. 8, No. 7. (1 October 1996), pp. 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 ...


Remarks on a recent paper on the "no free lunch" theorems

Evolutionary Computation, IEEE Transactions on In Evolutionary Computation, IEEE Transactions on, Vol. 5, No. 3. (June 2001), pp. 295-296,


This note discusses the recent paper "Some technical remarks on the proof of the no free lunch theorem" by Koppen (2000). In that paper, some technical issues related to the formal proof of the no free lunch (NFL) theorem for search were given by Wolpert and Macready (1995, 1997). The present authors explore the issues raised in that paper including the presentation of a simpler version of the NFL proof in accord with a suggestion made explicitly by Koppen (2000) and ...


No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1. (06 April 1997), pp. 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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