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JCM
2007

Generalization Capabilities Enhancement of a Learning System by Fuzzy Space Clustering

13 years 11 months ago
Generalization Capabilities Enhancement of a Learning System by Fuzzy Space Clustering
Abstract— We have used measurements taken on real network to enhance the performance of our radio network planning tool. A distribution learning technique is adopted to realize this challenged task. To ensure better generalization capabilities of the learning algorithm, a preprocessing of data is required and involves the use of a clustering algorithm that divides the whole learning space into subspaces. In this paper we apply a new fuzzy clustering algorithm to a prediction tool of a third generation (3G) cellular radio network. Results show that the differences observed between simulations and measurements can be considerably diminished and the generalization capacity is enhanced thanks to the proposed clustering algorithm. This algorithm performs well than classical k-means algorithm. We can then predict with enhanced accuracy new configuration for which we don’t have measurements, as long as they are not very different from learned configurations.
Zakaria Nouir, Berna Sayraç, Benoît F
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JCM
Authors Zakaria Nouir, Berna Sayraç, Benoît Fourestié, Walid Tabbara, Françoise Brouaye
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