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» Learning Causal Structure from Overlapping Variable Sets
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ANNPR
2008
Springer
13 years 9 months ago
Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network
Abstract. Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining fr...
Jean-François Connolly, Eric Granger, Rober...
WWW
2010
ACM
14 years 2 months ago
Stop thinking, start tagging: tag semantics emerge from collaborative verbosity
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the fact...
Christian Körner, Dominik Benz, Andreas Hotho...
ESOP
2011
Springer
12 years 11 months ago
Measure Transformer Semantics for Bayesian Machine Learning
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Johannes Borgström, Andrew D. Gordon, Michael...
ICML
2000
IEEE
14 years 8 months ago
Discovering Homogeneous Regions in Spatial Data through Competition
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...
Slobodan Vucetic, Zoran Obradovic
AAAI
1994
13 years 9 months ago
Learning to Explore and Build Maps
Using the methods demonstrated in this paper, a robot with an unknown sensorimotor system can learn sets of features and behaviors adequate to explore a continuous environment and...
David Pierce, Benjamin Kuipers