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» Incremental Learning of Relational Action Rules
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ICML
2009
IEEE
14 years 8 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
PVLDB
2010
106views more  PVLDB 2010»
13 years 5 months ago
Just-in-time Data Integration in Action
Today’s data integration systems must be flexible enough to support the typical iterative and incremental process of integration, and may need to scale to hundreds of data sour...
Martin Hentschel, Laura M. Haas, Renée J. M...
SOFSEM
2007
Springer
14 years 1 months ago
Incremental Learning of Planning Operators in Stochastic Domains
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
Javad Safaei, Gholamreza Ghassem-Sani
PAKDD
2005
ACM
103views Data Mining» more  PAKDD 2005»
14 years 29 days ago
Subgroup Discovery Techniques and Applications
This paper presents the advances in subgroup discovery and the ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explici...
Nada Lavrac
ISCI
1998
139views more  ISCI 1998»
13 years 7 months ago
A Rough Set Approach to Attribute Generalization in Data Mining
This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification...
Chien-Chung Chan