95 search results - page 11 / 19 » Classification of Random Boolean Networks |
JMLR
2010
13 years 4 months ago
2010
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
BMCBI
2010
13 years 10 months ago
2010
Background: The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the exp...
ECML
2006
Springer
14 years 1 months ago
2006
Springer
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...
ICML
2006
IEEE
14 years 10 months ago
2006
IEEE
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
GLOBECOM
2010
IEEE
13 years 7 months ago
2010
IEEE
While in computer networks the number of possible protocol encapsulations is growing day after day, network administrators face ever increasing difficulties in selecting accurately...