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» Learning to Classify Texts Using Positive and Unlabeled Data
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KDD
2009
ACM
269views Data Mining» more  KDD 2009»
14 years 8 months ago
Extracting discriminative concepts for domain adaptation in text mining
One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributi...
Bo Chen, Wai Lam, Ivor Tsang, Tak-Lam Wong
MM
2005
ACM
160views Multimedia» more  MM 2005»
14 years 1 months ago
Putting active learning into multimedia applications: dynamic definition and refinement of concept classifiers
The authors developed an extensible system for video exploitation that puts the user in control to better accommodate novel situations and source material. Visually dense displays...
Ming-yu Chen, Michael G. Christel, Alexander G. Ha...
WCE
2007
13 years 8 months ago
A Comparison of Classification Techniques for Technical Text Passages
— Our work explores the use of several text categorization techniques for classification of manufacturing quality defect and service shop data sets into fixed categories. Althoug...
Mark M. Kornfein, Helena Goldfarb
ICASSP
2011
IEEE
12 years 11 months ago
Deep belief nets for natural language call-routing
This paper considers application of Deep Belief Nets (DBNs) to natural language call routing. DBNs have been successfully applied to a number of tasks, including image, audio and ...
Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabha...
ICML
2000
IEEE
14 years 8 months ago
Incremental Learning in SwiftFile
SwiftFile is an intelligent assistant that helps users organize their e-mail into folders. SwiftFile uses a text classifier to predict where each new message is likely to be filed...
Richard Segal, Jeffrey O. Kephart