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» Learning from Multiple Sources of Inaccurate Data
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DAWAK
2006
Springer
13 years 11 months ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data source...
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, ...
ICDM
2002
IEEE
138views Data Mining» more  ICDM 2002»
14 years 12 days ago
Extraction Techniques for Mining Services from Web Sources
The Web has established itself as the dominant medium for doing electronic commerce. Consequently the number of service providers, both large and small, advertising their services...
Hasan Davulcu, Saikat Mukherjee, I. V. Ramakrishna...
ICDM
2003
IEEE
115views Data Mining» more  ICDM 2003»
14 years 23 days ago
On Precision and Recall of Multi-Attribute Data Extraction from Semistructured Sources
Machine learning techniques for data extraction from semistructured sources exhibit different precision and recall characteristics. However to date the formal relationship between...
Guizhen Yang, Saikat Mukherjee, I. V. Ramakrishnan
NAACL
2004
13 years 8 months ago
Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. W...
Katherine Forbes-Riley, Diane J. Litman
ICCS
2003
Springer
14 years 20 days ago
Virtual Telemetry for Dynamic Data-Driven Application Simulations
Abstract. We describe a virtual telemetry system that allows us to devise and augment dynamic data-driven application simulations (DDDAS). Virtual telemetry has the advantage that ...
Craig C. Douglas, Yalchin Efendiev, Richard E. Ewi...