A sequential algorithm for training text classifiers: corrigendum and additional data

Training algorithms for linear text classifiers

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval

Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval

Machine learning in automated text categorization

ACM Computing Surveys (CSUR)

Probabilistic combination of text classifiers using reliability indicators: models and results

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

Using asymmetric distributions to improve text classifier probability estimates

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval

The Combination of Text Classifiers Using Reliability Indicators

Information Retrieval

NEWPAR: an automatic feature selection and weighting schema for category ranking

Proceedings of the 2006 ACM symposium on Document engineering

Web-based text classification in the absence of manually labeled training documents

Journal of the American Society for Information Science and Technology

Learning to Classify Documents with Only a Small Positive Training Set

ECML '07 Proceedings of the 18th European conference on Machine Learning

Active Class Selection

ECML '07 Proceedings of the 18th European conference on Machine Learning

Learning to segment from a few well-selected training images

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning

Active learning for part-of-speech tagging: accelerating corpus annotation

LAW '07 Proceedings of the Linguistic Annotation Workshop

LUBM: A benchmark for OWL knowledge base systems

Web Semantics: Science, Services and Agents on the World Wide Web

Negative training data can be harmful to text classification

EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing