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» Learning relational dependency networks in hybrid domains
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ECAI
2004
Springer
14 years 4 months ago
Exploiting Association and Correlation Rules - Parameters for Improving the K2 Algorithm
A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between dif...
Evelina Lamma, Fabrizio Riguzzi, Sergio Storari
FUIN
2008
108views more  FUIN 2008»
13 years 9 months ago
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
Wannes Meert, Jan Struyf, Hendrik Blockeel
SEAL
1998
Springer
14 years 3 months ago
Co-evolution, Determinism and Robustness
Abstract. Robustness has long been recognised as a critical issue for coevolutionary learning. It has been achieved in a number of cases, though usually in domains which involve so...
Alan D. Blair, Elizabeth Sklar, Pablo Funes
ICFP
2009
ACM
14 years 11 months ago
Safe functional reactive programming through dependent types
Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on signals. FRP is based on the synchr...
Neil Sculthorpe, Henrik Nilsson
TPDS
2002
126views more  TPDS 2002»
13 years 10 months ago
P-3PC: A Point-to-Point Communication Model for Automatic and Optimal Decomposition of Regular Domain Problems
One of the most fundamental problems automatic parallelization tools are confronted with is to find an optimal domain decomposition for a given application. For regular domain prob...
Frank J. Seinstra, Dennis Koelma