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AII
1992
13 years 9 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma
CAISE
2010
Springer
13 years 6 months ago
Probabilistic Models to Reconcile Complex Data from Inaccurate Data Sources
There is a large amount of data that is published on the Web and several techniques have been developed to extract and integrate data from Web sources. However, Web data are inhere...
Lorenzo Blanco, Valter Crescenzi, Paolo Merialdo, ...
ICRA
2009
IEEE
179views Robotics» more  ICRA 2009»
13 years 11 months ago
Automatic weight learning for multiple data sources when learning from demonstration
— Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the Learning from Demonstration paradigm. Here a polic...
Brenna Argall, Brett Browning, Manuela M. Veloso
NIPS
2007
13 years 6 months ago
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classi...
John Blitzer, Koby Crammer, Alex Kulesza, Fernando...
COLT
1989
Springer
13 years 9 months ago
Learning in the Presence of Inaccurate Information
The present paper considers the effects of introducing inaccuracies in a learner’s environment in Gold’s learning model of identification in the limit. Three kinds of inaccu...
Mark A. Fulk, Sanjay Jain