Elements of information theory
Elements of information theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Efficient greedy learning of Gaussian mixture models
Neural Computation
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Liveclassifier: creating hierarchical text classifiers through web corpora
Proceedings of the 13th international conference on World Wide Web
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Large-scale hierarchical text classification without labelled data
Proceedings of the fourth ACM international conference on Web search and data mining
Data Mining and Knowledge Discovery
Artificial immune system for illicit content identification in social media
Journal of the American Society for Information Science and Technology
Sampling the Web as Training Data for Text Classification
International Journal of Digital Library Systems
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Most text classification techniques assume that manually labeled documents (corpora) can be easily obtained while learning text classifiers. However, labeled training documents are sometimes unavailable or inadequate even if they are available. The goal of this article is to present a self-learned approach to extract high-quality training documents from the Web when the required manually labeled documents are unavailable or of poor quality. To learn a text classifier automatically, we need only a set of user-defined categories and some highly related keywords. Extensive experiments are conducted to evaluate the performance of the proposed approach using the test set from the Reuters-21578 news data set. The experiments show that very promising results can be achieved only by using automatically extracted documents from the Web. © 2007 Wiley Periodicals, Inc.