Machine Learning
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Multiagent Collaborative Learning for Distributed Business Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
SMILE: Sound Multi-agent Incremental LEarning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Rule-based microarray classification system using multi-agent approach
AIC'04 Proceedings of the 4th WSEAS International Conference on Applied Informatics and Communications
Multi-agent learning: how to interact to improve collective results
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
Multi-agent based classification using argumentation from experience
Autonomous Agents and Multi-Agent Systems
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Multiagent learning differs from standard machine learning in that most existing learning methods assume that all knowledge is available locally in a single agent. In multiagent systems, this assumption does not hold because relevant knowledge is distributed among the agents within the system. We describe a decentralized learning algorithm for {\it distributed classification tasks}, i.e. classification when the attributes are distributed among a set of agents and cannot be gathered into a central agent. Our main contribution is to introduce and formalize the distributed classfication task, show that existing classification algorithms are not satisfactory for distributed classification tasks, and finally, to show that our collaborative learning algorithm performs well at distributed classification.