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» Learning for Dynamic Subsumption
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ICML
2009
IEEE
16 years 3 months ago
Efficient learning algorithms for changing environments
We study online learning in an oblivious changing environment. The standard measure of regret bounds the difference between the cost of the online learner and the best decision in...
Elad Hazan, C. Seshadhri
KDD
2003
ACM
150views Data Mining» more  KDD 2003»
16 years 3 months ago
Learning relational probability trees
Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probabili...
Jennifer Neville, David Jensen, Lisa Friedland, Mi...
AMEC
2003
Springer
15 years 8 months ago
Improving Learning Performance by Applying Economic Knowledge
Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this spac...
Christopher H. Brooks, Robert S. Gazzale, Jeffrey ...
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MCS
2009
Springer
15 years 7 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
ECAI
2006
Springer
15 years 6 months ago
Learning Behaviors Models for Robot Execution Control
Robust execution of robotic tasks is a difficult problem. In many situations, these tasks involve complex behaviors combining different functionalities (e.g. perception, localizat...
Guillaume Infantes, Félix Ingrand, Malik Gh...