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EUSFLAT
2003
152views Fuzzy Logic» more  EUSFLAT 2003»
13 years 6 months ago
Bayesian networks for continuous values and uncertainty in the learning process
This paper proposes a method for Bayesian networks that handles uncertainty and discretization of continuous variables when learning the networks from a database of cases. The dat...
J. F. Baldwin, E. Di Tomaso
ICML
1996
IEEE
14 years 5 months ago
Discretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Moisés Goldszmidt, Nir Friedman
ICANN
2009
Springer
13 years 8 months ago
Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data
In a typical reinforcement learning (RL) setting details of the environment are not given explicitly but have to be estimated from observations. Most RL approaches only optimize th...
Alexander Hans, Steffen Udluft
ICTAI
2005
IEEE
13 years 10 months ago
A Dynamic Bayesian Network for Handling Uncertainty in a Decision Support System Adapted to the Monitoring of Patients Treated b
Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripher...
Cédric Rose, Cherif Smaili, François...
JMLR
2010
125views more  JMLR 2010»
12 years 11 months ago
Continuous Time Bayesian Network Reasoning and Learning Engine
We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...