Maximum Entropy Stochastic Tasks Classification
Miguel Ángel Toledo, J. J. Medel · Research in Computing Science · 2006
In this work we introduce a task classification model for soft real-time systems. Task processes are analyzed as probabilistic distribution functions which parameters are considered as stationary random variables over the long time. We present a technique for tasks classification based on the maximum entropy level as element of differentiation among tasks. The classification model is described and evaluated based on probabilistic distribution functions properties