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» Explaining inferences in Bayesian networks
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NIPS
1992
13 years 8 months ago
Explanation-Based Neural Network Learning for Robot Control
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...
Tom M. Mitchell, Sebastian Thrun
EWSN
2004
Springer
14 years 7 months ago
Context-Aware Sensors
Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring...
Eiman Elnahrawy, Badri Nath
ERCIMDL
2008
Springer
112views Education» more  ERCIMDL 2008»
13 years 9 months ago
Releasing the Power of Digital Metadata: Examining Large Networks of Co-related Publications
Bibliographic metadata plays a key role in scientific literature, not only to summarise and establish the facts of the publication record, but also to track citations between publ...
David Tarrant, Les Carr, Terry R. Payne
CHI
2006
ACM
14 years 8 months ago
Quantifying interpersonal influence in face-to-face conversations based on visual attention patterns
A novel measure for automatically quantifying the amount of interpersonal influence present in face-toface conversations is proposed based on the visualattention patterns of the p...
Kazuhiro Otsuka, Junji Yamato, Yoshinao Takemae, H...
IAT
2008
IEEE
14 years 2 months ago
Finding Minimum Data Requirements Using Pseudo-independence
In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context ...
Yoonheui Kim, Victor R. Lesser