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» Learning Classifiers from Semantically Heterogeneous Data
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JAIR
2006
110views more  JAIR 2006»
13 years 7 months ago
Domain Adaptation for Statistical Classifiers
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applicati...
Hal Daumé III, Daniel Marcu
LREC
2008
110views Education» more  LREC 2008»
13 years 9 months ago
Unsupervised and Domain Independent Ontology Learning: Combining Heterogeneous Sources of Evidence
Acquiring knowledge from the Web to build domain ontologies has become a common practice in the Ontological Engineering field. The vast amount of freely available information allo...
David Manzano-Macho, Asunción Gómez-...
ICPR
2008
IEEE
14 years 8 months ago
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
CVPR
2009
IEEE
15 years 2 months ago
A Min-Max Framework of Cascaded Classifier with Multiple Instance Learning for Computer Aided Diagnosis
The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characterist...
Dijia Wu (Rensselaer Polytechnic Institute), Jinbo...
JODS
2008
152views Data Mining» more  JODS 2008»
13 years 7 months ago
Deploying Semantic Web Services-Based Applications in the e-Government Domain
Joining up services in e-Government usually implies governmental agencies acting in concert without a central control regime. This requires to the sharing scattered and heterogeneo...
Alessio Gugliotta, John Domingue, Liliana Cabral, ...