Abstract. This paper is concerned with the reliable inference of optimal treeapproximations to the dependency structure of an unknown distribution generating data. The traditional ...
This article presents a new evolutionary algorithm (EA) for induction of mixed decision trees. In nonterminal nodes of a mixed tree, different types of tests can be placed, rangin...
In this paper we study the covariance structure of the number of nodes k and l steps away from the root in random recursive trees. We give an analytic expression valid for all k, ...
Remco van der Hofstad, Gerard Hooghiemstra, Piet V...
In this paper, we present an efficient method to solve the obstacle-avoiding rectilinear Steiner minimum tree (OARSMT) problem optimally. Our work is a major improvement over the w...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...