824 search results - page 71 / 165 » Learning probabilistic logic models from probabilistic examp... |
137
Voted
NN
1997
Springer
15 years 8 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
180
Voted
ICLA
2011
Springer
14 years 7 months ago
2011
Springer
We propose a probabilistic interpretation of Propositional Dynamic Logic (PDL). We show that logical and behavioral equivalence are equivalent over general measurable spaces. This...
263
click to vote
EH
1999
IEEE
1999
IEEE
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
15 years 8 months ago
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
128
click to vote
ICML
1997
IEEE
16 years 4 months ago
1997
IEEE
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
141
click to vote
BIBE
2007
IEEE
15 years 10 months ago
2007
IEEE
—High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray ana...