Generalization performance of support vector classifiers for density level detection

Generalization performance of support vector classifiers for density level detection

This paper investigates the generalization performance of support vector classifiers for density level detection (DLD) when the input term belongs to a separable Hilbert space. The estimate of learning rate for DLD problem is established by Rademacher average and iterative techniques, which is indep...

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Veröffentlicht in:Neurocomputing (Amsterdam) 2013-11, Vol.119, p.434-438
Hauptverfasser: Chen, Hong, Zhou, Yicong, Tang, Yi, Tang, Yuan Yan, Pan, Zhibin
Format: Artikel
Sprache:eng
Schlagworte:
Applied sciences
Classifiers
Computer science
control theory
systems
Covering
Data processing. List processing. Character string processing
Density
Density level detection
Estimates
Exact sciences and technology
Hilbert space
Iterative technique
Learning
Learning rate
Mathematical analysis
Memory organisation. Data processing
Rademacher average
Software
Vectors (mathematics)
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Beschreibung
Zusammenfassung:This paper investigates the generalization performance of support vector classifiers for density level detection (DLD) when the input term belongs to a separable Hilbert space. The estimate of learning rate for DLD problem is established by Rademacher average and iterative techniques, which is independent of the assumption of covering number used in the previous literature.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.03.014