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» Modeling Classification and Inference Learning
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IUI
2004
ACM
14 years 1 months ago
Choosing when to interact with learners
In this paper, we describe a method for pedagogical agents to choose when to interact with learners in interactive learning environments. This method is based on observations of h...
Lei Qu, Ning Wang, W. Lewis Johnson
BMCBI
2006
118views more  BMCBI 2006»
13 years 8 months ago
Predicting the effect of missense mutations on protein function: analysis with Bayesian networks
Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, ...
Chris J. Needham, James R. Bradford, Andrew J. Bul...
SECON
2010
IEEE
13 years 5 months ago
Deconstructing Interference Relations in WiFi Networks
Abstract--Wireless interference is the major cause of degradation of capacity in 802.11 wireless networks. We present an approach to estimate the interference between nodes and lin...
Anand Kashyap, Utpal Paul, Samir R. Das
INTERSPEECH
2010
13 years 2 months ago
Deep-structured hidden conditional random fields for phonetic recognition
We extend our earlier work on deep-structured conditional random field (DCRF) and develop deep-structured hidden conditional random field (DHCRF). We investigate the use of this n...
Dong Yu, Li Deng
BC
2010
163views more  BC 2010»
13 years 5 months ago
Action and behavior: a free-energy formulation
We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz's agenda to understand the brain in terms of energy ...
Karl J. Friston, Jean Daunizeau, James Kilner, Ste...