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ICML
2004
IEEE
14 years 9 months ago
A MFoM learning approach to robust multiclass multi-label text categorization
We propose a multiclass (MC) classification approach to text categorization (TC). To fully take advantage of both positive and negative training examples, a maximal figure-of-meri...
Sheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
14 years 9 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
SIGIR
2003
ACM
14 years 1 months ago
Text categorization by boosting automatically extracted concepts
Term-based representations of documents have found widespread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard le...
Lijuan Cai, Thomas Hofmann
ICML
2002
IEEE
14 years 9 months ago
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amountoflabeled datarequired is t...
Rayid Ghani
CIKM
2005
Springer
14 years 2 months ago
A novel refinement approach for text categorization
In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to succes...
Songbo Tan, Xueqi Cheng, Moustafa Ghanem, Bin Wang...