Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML)
EWSL '91 Proceedings of the European Working Session on Machine Learning
Techniques for Estimating the Computation and Communication Costs of Distributed Data Mining
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Creating Ensembles of Classifiers
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Learning from Multiple Bayesian Networks for the Revision and Refinement of Expert Systems
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
Multiagent Collaborative Learning for Distributed Business Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Scalable Representative Instance Selection and Ranking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
International Journal of Hybrid Intelligent Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
Distributed interactive learning in multi-agent systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Agent influence as a predictor of difficulty for decentralized problem-solving
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
MALEF: Framework for distributed machine learning and data mining
International Journal of Intelligent Information and Database Systems
An A-Team approach to learning classifiers from distributed data sources
International Journal of Intelligent Information and Database Systems
JABAT middleware as a tool for solving optimization problems
Transactions on computational collective intelligence II
Distributed learning with data reduction
Transactions on computational collective intelligence IV
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
Editorial: Large scale instance selection by means of federal instance selection
Data & Knowledge Engineering
Fuzzy generalised classifier for distributed knowledge discovery
International Journal of Business Intelligence and Data Mining
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Distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown.