Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Processing and Management: an International Journal
Location Based and Customized Voice Information Service for Mobile Community
Information Systems Frontiers
Incremental profile learning based on a reinforcement method
Proceedings of the 2005 ACM symposium on Applied computing
Adaptive sampling for thresholding in document filtering and classification
Information Processing and Management: an International Journal
Dynamic category profiling for text filtering and classification
Information Processing and Management: an International Journal
Interactive high-quality text classification
Information Processing and Management: an International Journal
Automatically identifying localizable queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Computers in Biology and Medicine
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Text and hypertext categorization
Artificial intelligence
Content-based filtering in on-line social networks
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Automatic indexing of news videos through text classification techniques
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Text categorization using SVMs with rocchio ensemble for internet information classification
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
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Several machine learning algorithms have recently been used for text categorization and filtering. In particular, boosting methods such as AdaBoost have shown good performance applied to real text data. However, most of existing boosting algorithms are based on classifiers that use binary-valued features. Thus, they do not fully make use of the weight information provided by standard term weighting methods. In this paper, we present a boosting-based learning method for text filtering that uses naive Bayes classifiers as a weak learner. The use of naive Bayes allows the boosting algorithm to utilize term frequency information while maintaining probabilistically accurate confidence ratio. Applied to TREC-7 and TREC-8 filtering track documents, the proposed method obtained a significant improvement in LF1, LF2, F1 and F3 measures compared to the best results submitted by other TREC entries.