The conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate.Expand

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… Expand

We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms… Expand

It is shown that in practice the naive frequency based estimation of the decompo sition terms is by itself biased and how to correct for this bias is correct.Expand

This paper surveys the emerging science of how to design a “COllective INtelligence” (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized communication or control.… Expand

It is shown that one cannot say: if empirical misclassification rate is low, the Vapnik-Chervonenkis dimension of your generalizer is small, and the training set is large, then with high probability your OTS error is small.Expand

This paper presents an introduction to the science of such systems of self-interested agents, which are often very large, distributed, and support little if any centralized communication and control, although those characteristics are not part of their formal definition.Expand

This paper reviews the supervised learning versions of the no-free-lunch theorems in a simplified form. It also discusses the significance of those theorems, and their relation to other aspects of… Expand

It is demonstrated experimentally that using these new utility functions can result in significantly improved performance over that of previously investigated COIN payoff utilities, over and above those previous utilities' superiority to the conventional team game utility.Expand

This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both bounded rationality and mean field theory in statistical physics, and shows that those topics are fundamentally one and the same.Expand