Most process models calibrate their internal settings using historical data. Collecting this data is expensive, tedious, and often an incomplete process. Is it possible to make acc...
Tim Menzies, Oussama El-Rawas, Barry W. Boehm, Ray...
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
Trust management model that we present is adapted for ubiquitous devices cooperation, rather than for classic client-supplier relationship. We use fuzzy numbers to represent trust...
Existing retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model’s empirica...
We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained "on the fly...