SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering

This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account varia...

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Veröffentlicht in:	Neural networks Jg. 21; H. 9; S. 1287 - 1301
Hauptverfasser:	Amadou Boubacar, Habiboulaye, Lecoeuche, Stéphane, Maouche, Salah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.11.2008 Elsevier
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Cluster Analysis Evolving models Exact sciences and technology Flows in networks. Combinatorial problems Information, signal and communications theory Models, Statistical Multi-class problems Non-stationary data Online clustering Online Systems Operational research and scientific management Operational research. Management science Risk RKHS Signal and communications theory Signal representation. Spectral analysis Signal, noise Stochastic Processes Telecommunications and information theory Online clustering RKHS Non-stationary data Multi-class problems Evolving models Cluster analysis Automatic classification On line Similarity Adaptability Support vector machine Approximation algorithm Combinatorial optimization Adaptive method Hilbert space Architecture Theoretical study Error bound Non stationary condition On-line systems Experimental study Signal classification Unsupervised learning Kernel method Simulation Metric Comparative study
ISSN:	0893-6080, 1879-2782
Online-Zugang:	Volltext
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