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ICDM
2008
IEEE
120views Data Mining» more  ICDM 2008»
15 years 8 months ago
Predicting Future Decision Trees from Evolving Data
Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understa...
Mirko Böttcher, Martin Spott, Rudolf Kruse
ECML
2007
Springer
15 years 5 months ago
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
Abstract. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...
Anneleen Van Assche, Hendrik Blockeel
IEEEICCI
2009
IEEE
14 years 11 months ago
Learning from an ensemble of Receptive Fields
Abstract-In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as Shape Encodi...
Hanlin Goh, Joo Hwe Lim, Chai Quek
GECCO
2003
Springer
182views Optimization» more  GECCO 2003»
15 years 6 months ago
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Allan Tucker, Xiaohui Liu, David Garway-Heath
IJCAI
2001
15 years 2 months ago
Mining Soft-Matching Rules from Textual Data
Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although exist...
Un Yong Nahm, Raymond J. Mooney