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FUZZIEEE
2007
IEEE
15 years 11 months ago
Learning Undirected Possibilistic Networks with Conditional Independence Tests
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
Christian Borgelt
144
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AHS
2007
IEEE
208views Hardware» more  AHS 2007»
15 years 6 months ago
Evolving Redundant Structures for Reliable Circuits - Lessons Learned
Fault Tolerance is an increasing challenge for integrated circuits due to semiconductor technology scaling. This paper looks at how artificial evolution may be tuned to the creat...
Asbjørn Djupdal, Pauline C. Haddow
ICALP
2004
Springer
15 years 10 months ago
Learning a Hidden Subgraph
We consider the problem of learning a labeled graph from a given family of graphs on n vertices in a model where the only allowed operation is to query whether a set of vertices i...
Noga Alon, Vera Asodi
ICRA
2007
IEEE
155views Robotics» more  ICRA 2007»
15 years 11 months ago
Dogged Learning for Robots
— Ubiquitous robots need the ability to adapt their behaviour to the changing situations and demands they will encounter during their lifetimes. In particular, non-technical user...
Daniel H. Grollman, Odest Chadwicke Jenkins
ACSAC
2006
IEEE
15 years 11 months ago
Practical Attack Graph Generation for Network Defense
Attack graphs are a valuable tool to network defenders, illustrating paths an attacker can use to gain access to a targeted network. Defenders can then focus their efforts on patc...
Kyle Ingols, Richard Lippmann, Keith Piwowarski