Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Distributed cooperative Bayesian learning strategies
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Parallel and Distributed Data Mining: An Introduction
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
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In this paper, we propose a new approach to apply metalearning concept to distributed data mining. We name this approach Knowledge Probing where a supervised learning process is organised into two learning stages. In the first learning phase, a set of base classifiers are learned in parallel from a distributed data set. In the second learning phase, meta-learning is applied to induce the relationship between an attribute vector and the class predictions from all the base classifiers. By applying this approach to an environment where base classifiers are produced from distributed data sources, the output of Knowledge Probing process can be viewed as the assimilated knowledge of that distributed learning system. Some initial experimental results on the quality of the assimilated knowledge are presented. We believe that an integration of Knowledge Probing technique and the available data mining algorithms can provide a practical framework for distributed data mining applications.