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» Learning to Classify Texts Using Positive and Unlabeled Data
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NLP
2000
13 years 11 months ago
Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characte...
Georgios Paliouras, Vangelis Karkaletsis, Ion Andr...
WWW
2007
ACM
14 years 8 months ago
Efficient training on biased minimax probability machine for imbalanced text classification
The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. In this paper, we propose a Second Order Cone Programming (SO...
Xiang Peng, Irwin King
JASIS
2006
96views more  JASIS 2006»
13 years 7 months ago
Learning to classify documents according to genre
Genre or style analysis can be used to improve results achieved using standard IR techniques. A genre class is a group of documents that are written in a similar style. Genre clas...
Aidan Finn, Nicholas Kushmerick
ECCV
2010
Springer
13 years 12 months ago
Efficient Highly Over-Complete Sparse Coding using a Mixture Model
Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data...
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