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» Learning to Classify Texts Using Positive and Unlabeled Data
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ICML
2000
IEEE
14 years 8 months ago
Solving the Multiple-Instance Problem: A Lazy Learning Approach
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being...
Jun Wang, Jean-Daniel Zucker
WWW
2010
ACM
13 years 7 months ago
Cross-domain sentiment classification via spectral feature alignment
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditio...
Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qian...
AI
2007
Springer
13 years 11 months ago
Performance Measures in Classification of Human Communications
This study emphasizes the importance of using appropriate measures in particular text classification settings. We focus on methods that evaluate how well a classifier performs. The...
Marina Sokolova, Guy Lapalme
WWW
2011
ACM
13 years 2 months ago
Automatic construction of a context-aware sentiment lexicon: an optimization approach
The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people’s sentiments toward different topics. In most sentiment analy...
Yue Lu, Malú Castellanos, Umeshwar Dayal, C...
CVPR
2010
IEEE
14 years 2 months ago
Improving web-image search results using query-relative classifiers
Web image search using text queries has received considerable attention. However, current state-of-the-art approaches require training models for every new query, and are therefore...
Josip Krapac, Moray Allan, Jakob Verbeek, Frédér...