889 search results - page 27 / 178 » Observational Learning in Random Networks |
NIPS
1992
13 years 10 months ago
1992
How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data se...
ICIC
2005
Springer
14 years 3 months ago
2005
Springer
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number...
ICML
2003
IEEE
14 years 10 months ago
2003
IEEE
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
ICML
2009
IEEE
14 years 10 months ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
ECCV
2008
Springer
14 years 11 months ago
2008
Springer
Recently, models based on conditional random fields (CRF) have produced promising results on labeling sequential data in several scientific fields. However, in the vision task of c...