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» A New Way to Introduce Knowledge into Reinforcement Learning
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COLT
2008
Springer
15 years 1 months ago
Linear Algorithms for Online Multitask Classification
We design and analyze interacting online algorithms for multitask classification that perform better than independent learners whenever the tasks are related in a certain sense. W...
Giovanni Cavallanti, Nicolò Cesa-Bianchi, C...
ATAL
2006
Springer
15 years 3 months ago
Efficient agent-based cluster ensembles
Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified cl...
Adrian K. Agogino, Kagan Tumer
FLAIRS
1998
15 years 1 months ago
Interactively Training Pixel Classifiers
Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. T...
Justus H. Piater, Edward M. Riseman, Paul E. Utgof...
MCS
2009
Springer
15 years 4 months ago
Incremental Learning of Variable Rate Concept Drift
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
Ryan Elwell, Robi Polikar
VRML
2010
ACM
15 years 3 months ago
Cognitive scaffolding in Web3D learning systems: a case study for form and structure
In this paper, we describe a case study in usability engineering for Web3D learning systems and introduce a new step to the typical methods of the usability design. Pedagogical ap...
Felipe Bacim, Nicholas F. Polys, Jian Chen, Mehdi ...