8970 search results - page 174 / 1794 » Learning to Learn Causal Models |
138
Voted
AAAI
2007
15 years 6 months ago
2007
The importance of the efforts towards integrating the symbolic and connectionist paradigms of artificial intelligence has been widely recognised. Integration may lead to more e...
166
Voted
AAAI
2008
15 years 6 months ago
2008
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
155
click to vote
JMLR
2010
2010
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
14 years 11 months ago
The presence of asymmetry in the misclassification costs or class prevalences is a common occurrence in the pattern classification domain. While much interest has been devoted to ...
126
click to vote
SEMWEB
2001
Springer
15 years 9 months ago
2001
Springer
Building repositories for e-learning is an iterative process and course content and course structure are always changing. We realized the necessity to separate content from structu...
137
click to vote
NIPS
1997
15 years 5 months ago
1997
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...