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BMCBI
2010
229views more  BMCBI 2010»
13 years 9 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
CORR
2009
Springer
242views Education» more  CORR 2009»
13 years 6 months ago
Adaptive Scheduling of Data Paths using Uppaal Tiga
Abstract. We apply Uppaal Tiga to automatically compute adaptive scheduling strategies for an industrial case study dealing with a state-of-the-art image processing pipeline of a p...
Israa AlAttili, Fred Houben, Georgeta Igna, Steffe...
ISBI
2008
IEEE
14 years 9 months ago
Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images
In this work we present a novel approach for learning nonhomogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective lear...
Noah Lee, Andrew F. Laine, Theodore R. Smith
CIE
2009
Springer
14 years 3 months ago
Computable Exchangeable Sequences Have Computable de Finetti Measures
Abstract. We prove a uniformly computable version of de Finetti’s theorem on exchangeable sequences of real random variables. In the process, we develop machinery for computably ...
Cameron E. Freer, Daniel M. Roy
SAC
2006
ACM
14 years 3 months ago
A probability analysis for candidate-based frequent itemset algorithms
This paper explores the generation of candidates, which is an important step in frequent itemset mining algorithms, from a theoretical point of view. Important notions in our prob...
Nele Dexters, Paul W. Purdom, Dirk Van Gucht