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KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 1 months ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz
LREC
2010
136views Education» more  LREC 2010»
14 years 11 months ago
Base Concepts in the African Languages Compared to Upper Ontologies and the WordNet Top Ontology
Ontologies, and in particular upper ontologies, are foundational to the establishment of the Semantic Web. Upper ontologies are used as equivalence formalisms between domain speci...
Winston Anderson, Laurette Pretorius, Albert E. Ko...
FLAIRS
2003
14 years 11 months ago
Topic Extraction and Extension to Support Concept Mapping
Successful knowledge management may depend not only on knowledge capture, but on knowledge construction—on formulating new and useful knowledge that was not previously available...
David B. Leake, Ana Gabriela Maguitman, Thomas Rei...
EDM
2008
108views Data Mining» more  EDM 2008»
14 years 11 months ago
Do Students Who See More Concepts in an ITS Learn More?
Active engagement in the subject material has been strongly linked to deeper learning. In traditional teaching environments, even though the student might be presented with new con...
Moffat Mathews, Tanja Mitrovic
ICMLA
2007
14 years 11 months ago
Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text
We apply a new active learning formulation to the problem of learning medical concepts from unstructured text. The new formulation is based on maximizing the mutual information th...
Rómer Rosales, Praveen Krishnamurthy, R. Bh...