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IJCAI
2003
13 years 8 months ago
Learning to Classify Texts Using Positive and Unlabeled Data
In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a ...
Xiaoli Li, Bing Liu
ADMA
2010
Springer
248views Data Mining» more  ADMA 2010»
13 years 5 months ago
Classification Inductive Rule Learning with Negated Features
This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generat...
Stephanie Chua, Frans Coenen, Grant Malcolm
ICTIR
2009
Springer
13 years 5 months ago
Training Data Cleaning for Text Classification
Abstract. In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain; strategies are thus needed for maximizing t...
Andrea Esuli, Fabrizio Sebastiani
ESWA
2006
149views more  ESWA 2006»
13 years 7 months ago
An effective refinement strategy for KNN text classifier
Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an...
Songbo Tan
CVPR
2009
IEEE
15 years 2 months ago
Active Learning for Large Multi-class Problems
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods ...
Prateek Jain (University of Texas at Austin), Ashi...