SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Using Decision Trees to Construct a Practical Parser
Machine Learning - Special issue on natural language learning
Feature selection in SVM text categorization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
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In natural language tasks like text categorization, we usually have an enormous amount of unlabeled data in addition to a small amount of labeled data. We present here a transductive boosting method for text categorization in order to make use of the large amount of unlabeled data efficiently. Our experiments show that the transductive method outperforms conventional boosting techniques that employ only labeled data.