Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Intelligence Review - Special issue on lazy learning
Collaborative multiagent learning for classification tasks
Proceedings of the fifth international conference on Autonomous agents
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Naive Bayesian Classifier Committees
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Software Agents for Electronic Business: Opportunities and Challenges
Proceedings of the 9th ECCAI-ACAI/EASSS 2001, AEMAS 2001, HoloMAS 2001 on Multi-Agent-Systems and Applications II-Selected Revised Papers
Integrated Multi-agent-based Supply Chain Management
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
A Multiagent Approach for Logistics Performance Prediction Using Historical and Context Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Multiagent Approach for Logistics Performance Prediction Using Historical and Context Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Prototype selection algorithms for distributed learning
Pattern Recognition
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Ontology issue in multi-agent distributed learning
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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This paper presents a multiagent architecture and algorithms for collaborative learning in distributed and heterogeneous business systems, where the participating agents have local, incomplete knowledge used to make predictions about parameters of a business transaction.We propose two collaborative learning strategies which differ in the nature and amount of information that is exchanged during collaboration, and which are hence suitable for different organisational settings. The first algorithm relies on the exchange of information about a transaction instance, whereas the second algorithm uses qualitative information provided by individual agents, such as the results of predictions from the agentýs local perspective. We apply the architecture and strategies to a distributed supply chain prediction problem. Experiments run on a large real-world order data set indicate that our approach effectively improves the learning performance based on limited additional communication between the participating agents.