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» Learning to Classify Texts Using Positive and Unlabeled Data
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TREC
2004
13 years 8 months ago
Feature Generation, Feature Selection, Classifiers, and Conceptual Drift for Biomedical Document Triage
We approached the problem of classifying papers for the TREC 2004 Genomics Track triage task as a four step process: feature generation, feature selection, classifier training, an...
Aaron M. Cohen, Ravi Teja Bhupatiraju, William R. ...
CVPR
2008
IEEE
14 years 9 months ago
Unsupervised discovery of visual object class hierarchies
Objects in the world can be arranged into a hierarchy based on their semantic meaning (e.g. organism ? animal ? feline ? cat). What about defining a hierarchy based on the visual ...
Josef Sivic, Bryan C. Russell, Andrew Zisserman, W...
DGO
2008
126views Education» more  DGO 2008»
13 years 9 months ago
Active learning for e-rulemaking: public comment categorization
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
Stephen Purpura, Claire Cardie, Jesse Simons
AIM
2005
13 years 7 months ago
Semantic Integration in Text: From Ambiguous Names to Identifiable Entities
Intelligent access to information requires semantic integration of structured databases with unstructured textual resources. While the semantic integration problem has been widely...
Xin Li, Paul Morie, Dan Roth
CVPR
2008
IEEE
14 years 9 months ago
A joint appearance-spatial distance for kernel-based image categorization
The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures ...
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang,...