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We propose a novel nearest-neighbors approach to organize and curb the growth of radial basis function network in kernel adaptive filtering (KAF). The nearest-instance-centroid-estimation (NICE) kernel least-mean-square (KLMS) algorithm provides an appropriate time-space tradeoff with good performance. Its centers in the input/feature space are organized by quasi-orthogonal regions for greatly simplified...
The difficulties of online clustering are how to handle variation of cluster number in the same framework, how to be computationally efficient for real time applications, and how to ensure the error convergence of the algorithm. This paper presents a new online clustering algorithm that combines the self-adaptive neural network and support vector machine, which is used to learn continuously evolving...
This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments...
The mechanism for a peer to randomly choose logical neighbors, without any knowledge about the physical topology, causes a serious topology mismatch between the overlay networks and the physical network. A flooding-based search is robust to node failure, but the response message will be lost if any node on the response path fails, and the flooding search may causes heavy network traffic. In order...
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