316 search results - page 19 / 64 » Genomic computing networks learn complex POMDPs |
ICCV
2009
IEEE
13 years 5 months ago
2009
IEEE
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
GECCO
2009
Springer
14 years 6 days ago
2009
Springer
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
BMCBI
2010
13 years 7 months ago
2010
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
GECCO
2007
Springer
14 years 1 months ago
2007
Springer
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
ITICSE
2010
ACM
13 years 7 months ago
2010
ACM
Teaching wireless sensor networks (WSNs) at the undergraduate level is both challenging and rewarding. WSNs include low-level programming and debugging, power-aware operations, no...