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» Learning Classifiers from Semantically Heterogeneous Data
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IJCAI
1989
13 years 10 months ago
Noise-Tolerant Instance-Based Learning Algorithms
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...
David W. Aha, Dennis F. Kibler
SDM
2010
SIAM
259views Data Mining» more  SDM 2010»
13 years 11 months ago
Semi-supervised Bio-named Entity Recognition with Word-Codebook Learning
We describe a novel semi-supervised method called WordCodebook Learning (WCL), and apply it to the task of bionamed entity recognition (bioNER). Typical bioNER systems can be seen...
Pavel P. Kuksa, Yanjun Qi
ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
13 years 9 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
EJC
2010
13 years 4 months ago
Inferencing in Database Semantics
As a computational model of natural language communication, Database Semantics1 (DBS) includes a hearer mode and a speaker mode. For the content to be mapped into language expressi...
Roland Hausser
ICML
2004
IEEE
14 years 10 months ago
Semi-supervised learning using randomized mincuts
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...