Communications of the ACM - Special issue on parallelism
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
A self-improving helpdesk service system using case-based reasoning techniques
Computers in Industry
Term-weighting approaches in automatic text retrieval
Readings in information retrieval
The SMART and SIRE experimental retrieval systems
Readings in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Maximizing Text-Mining Performance
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Summarization as feature selection for text categorization
Proceedings of the tenth international conference on Information and knowledge management
Leightweight Document Clustering
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Lightweight Collaborative Filtering Method for Binary-Encoded Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Data-intensive analytics for predictive modeling
IBM Journal of Research and Development
Automated generation of model cases for help-desk applications
IBM Systems Journal
A low-complexity reduced-reference print identification algorithm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
International Journal of Open Source Software and Processes
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For decades, researchers have been working on ways to process text for classification and queries by relevant- document retrieval. The authors describe a method that uses minimal data structures and lightweight algorithms to match new documents to those stored in a database.