A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Integrating Background Knowledge into Nearest-Neighbor Text Classification
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
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We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of training examples. The second method uses background knowledge to reexpress the training examples. The last method treats pieces of background knowledge as unlabeled examples, and actually classifies them. The choice of background knowledge affects each method's performance and we discuss which type of background knowledge is most useful for each specific method.