Recent research in decision theoretic planning has focussedon making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structur...
Craig Boutilier, Ronen I. Brafman, Christopher W. ...
Background: The reconstruction of genetic regulatory networks from microarray gene expression data has been a challenging task in bioinformatics. Various approaches to this proble...
Guanrao Chen, Peter Larsen, Eyad Almasri, Yang Dai
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improv...
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...