We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we ...
Martin Gebser, Carito Guziolowski, Mihail Ivanchev...
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
The use of the statistical technique of mixture model analysis as a tool for early prediction of fault-prone program modules is investigated. The Expectation-Maximum likelihood (E...
In this paper we illustrate a methodology for modeling and analyzing flexible feeders using generalized semi-Markov process (GSMP) models. Working through the simple case consisti...
Michael S. Branicky, Greg C. Causey, Roger D. Quin...
A technique is presented to predict the performance behavior of control circuits for a linear FIFO. The control circuit consists of a linear chain of RendezVous elements, also cal...
Jo C. Ebergen, Scott Fairbanks, Ivan E. Sutherland