Text representation for intelligent text retrieval: a classification-oriented view
Text-based intelligent systems
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
Information storage and retrieval
Information storage and retrieval
Interoperability for digital libraries worldwide
Communications of the ACM
Adaptive information agents in distributed textual environments
AGENTS '98 Proceedings of the second international conference on Autonomous agents
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Using query mediators for distributed searching in federated digital libraries
Proceedings of the fourth ACM conference on Digital libraries
Communications of the ACM
NCSTRL: design and deployment of a globally distributed digital library
Journal of the American Society for Information Science - digital libraries: Part 1
The influence of learning in the behavior of information retrieval adaptive agents
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Evolving intelligent text-based agents
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Automatic classification using supervised learning in a medical document filtering application
Information Processing and Management: an International Journal
A vector space model for automatic indexing
Communications of the ACM
Collaborative multiagent learning for classification tasks
Proceedings of the fifth international conference on Autonomous agents
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Mining the Web's Link Structure
Computer
Trends in Electronic Government: Managing Distributed Knowledge
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Invited papers—1: classification in information storage and retrieval
ACM '65 Proceedings of the 1965 20th national conference
Problem of the Global Brain and Multi-Agent Modeling
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
A Comparison Between Single-agent and Multi-agent Classification of Documents
IPDPS '01 Proceedings of the 10th Heterogeneous Computing Workshop â"" HCW 2001 (Workshop 1) - Volume 2
Multi-agent information classification using dynamic acquaintance lists
Journal of the American Society for Information Science and Technology
Multi-agent Technology for Distributed Data Mining and Classification
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
SIGIR 2003 workshop on distributed information retrieval
ACM SIGIR Forum
Digital libraries settling the score: 10 years hence and 10 before
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Automated text classification using a multi-agent framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Distributed multi-agent information filtering—A comparative study: Research Articles
Journal of the American Society for Information Science and Technology
Complexity - Understanding Complex Systems: Part II
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms.