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» Incremental learning with partial instance memory
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ECML
2005
Springer
15 years 3 months ago
Model-Based Online Learning of POMDPs
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony
COLT
2004
Springer
15 years 1 months ago
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
ICML
2005
IEEE
15 years 10 months ago
Combining model-based and instance-based learning for first order regression
T ORDER REGRESSION (EXTENDED ABSTRACT) Kurt Driessensa Saso Dzeroskib a Department of Computer Science, University of Waikato, Hamilton, New Zealand (kurtd@waikato.ac.nz) b Departm...
Kurt Driessens, Saso Dzeroski
EKAW
2004
Springer
15 years 3 months ago
Enhancing Ontological Knowledge Through Ontology Population and Enrichment
Abstract. Ontologies are widely used for capturing and organizing knowledge of a particular domain of interest. This knowledge is usually evolvable and therefore an ontology mainte...
Alexandros G. Valarakos, Georgios Paliouras, Vange...
IDA
2002
Springer
14 years 9 months ago
Online classification of nonstationary data streams
Most classification methods are based on the assumption that the data conforms to a stationary distribution. However, the real-world data is usually collected over certain periods...
Mark Last