Noise Fuzzy Learning Vector Quantization

Abstract:

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Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.

Info:

Periodical:

Key Engineering Materials (Volumes 439-440)

Edited by:

Yanwen Wu

Pages:

367-371

DOI:

10.4028/www.scientific.net/KEM.439-440.367

Citation:

X. H. Wu et al., "Noise Fuzzy Learning Vector Quantization", Key Engineering Materials, Vols. 439-440, pp. 367-371, 2010

Online since:

June 2010

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Price:

$35.00

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