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Kohonen learning vector quantization.
The disadvantage of the generalized learning vector quantization (GLVQ) and fuzzy generalization learning vector quantization (FGLVQ) algorithms is discussed in this paper.
Learning Vector Quantization and Bayesian (neural-network) methods were applied to characterize pattern of errors.
Furthermore, we have selected and employed the neural network based on learning vector quantization for correct classification.
Among unsupervised classifiers, self-organising map (SOM) and learning vector quantiser (LVQ) have also been used.
We propose a new adaptive active clustering scheme, based on an initial fuzzy c-means clustering and learning vector quantization.
And a revised generalized learning vector quantization (RGLVQ) algorithm is proposed to overcome the disadvantage of GLVQ and FGLVQ.
The fault diagnosis of planetary gear is eventually realized by applying the extracted kernel principal components and learning vector quantization (LVQ) neural network.
In addition, the learning vector quantization (LVQ) network was adopted to select the appropriate inputs to improve the efficiency of the training process.
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The last few years have seen great steps forward in particular types of machine learning: vector-based machine learning and deep learning.
In this paper we propose a variant of the Learning Vector Quantization (LVQ) classification algorithm, the Distortion Sensitive LVQ (DSLVQ), to be used for encoder design in decentralized estimation.
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