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CCIA
2005
Springer
15 years 3 months ago
A Preliminary Study on the Relaxation of Numeric Features in Planning
Modern AI planners use different strategies to simplify the complexity of current planning problems and turn them more affordable. In this paper, we present a new approach that div...
Antonio Garrido, Eva Onaindia, Donato Herná...
PKDD
2004
Springer
155views Data Mining» more  PKDD 2004»
15 years 3 months ago
Ensemble Feature Ranking
A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Sele...
Kees Jong, Jérémie Mary, Antoine Cor...
FLAIRS
2007
15 years 3 days ago
Random Subsets Support Learning a Mixture of Heuristics
Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the probl...
Smiljana Petrovic, Susan L. Epstein
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
15 years 10 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
AAAI
1998
14 years 11 months ago
A* with Bounded Costs
A key assumption of all problem-solving approaches based on utility theory, including heuristic search, is that we can assign a utility or cost to each state. This in turn require...
Brian Logan, Natasha Alechina