Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
CIKM '94 Proceedings of the third international conference on Information and knowledge management
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Proceedings of the 2007 ACM symposium on Applied computing
Immune Learning in a Dynamic Information Environment
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
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Information filtering is one of the most useful and challenging tasks for effective information access. It is concerned with dynamically adapting the distribution of information where both evolving user's interests and new incoming information are taken into account. In this paper, we present an innovative approach to text filtering based on the novelty detection principle. This approach relies on a specific learning model which allows both accurate online learning of user's profile and evaluation of the coherency of user's behaviour during his interaction with the system.We empirically analyse our approach and present experimental results on the Reuters-21578 benchmark. The obtained results bring out a significant enhancement of performance as compared to the widely used Rocchio's learning algorithm.