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GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 7 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
138
Voted
FUIN
2008
108views more  FUIN 2008»
15 years 1 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
109
Voted
JMLR
2010
111views more  JMLR 2010»
14 years 9 months ago
An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models
Logic-based probabilistic models (LBPMs) enable us to handle problems with uncertainty succinctly thanks to the expressive power of logic. However, most of LBPMs have restrictions...
Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato,...
142
Voted
SIGIR
2011
ACM
14 years 5 months ago
Fast context-aware recommendations with factorization machines
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So ...
Steffen Rendle, Zeno Gantner, Christoph Freudentha...
99
Voted
CI
2002
92views more  CI 2002»
15 years 2 months ago
Model Selection in an Information Economy: Choosing What to Learn
As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance...
Christopher H. Brooks, Robert S. Gazzale, Rajarshi...