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NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 1 months ago
Learning Dynamic Bayesian Networks
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...
Zoubin Ghahramani
ER
1994
Springer
128views Database» more  ER 1994»
14 years 1 months ago
A Normal Form Object-Oriented Entity Relationship Diagram
A normal form object-oriented entity relationship (OOER) diagram is presented to address a set of 00 data modelling issues, viz. the inability to judge the quality of an 00 schema,...
Tok Wang Ling, Pit Koon Teo
COLT
1989
Springer
14 years 1 months ago
Learning in the Presence of Inaccurate Information
The present paper considers the effects of introducing inaccuracies in a learner’s environment in Gold’s learning model of identification in the limit. Three kinds of inaccu...
Mark A. Fulk, Sanjay Jain
NIPS
2008
13 years 10 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
JASIS
2000
120views more  JASIS 2000»
13 years 8 months ago
Probabilistic datalog: Implementing logical information retrieval for advanced applications
In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Dat...
Norbert Fuhr