We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for conc...
This paper presents a Component-based Framework Development Process, of the Cardiology Domain. The Framework, called FrameCardio, was developed in 4 steps: 1Problem Domain Definit...
As decisions in cardiology increasingly rely on non-invasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best ...
Razvan Ioan Ionasec, Ingmar Voigt, Bogdan Georgesc...