A New Classification Algorithm: Optimally Generalized Learning Vector Quantization (OGLVQ)

Turgay Temel


We present a new Generalized Learning Vector Quantization classifier called Optimally Generalized Learning Vector Quantization based on a novel weight-update rule for learning labelled samples. The algorithm attains stable prototype/weight vector dynamics in terms of estimated current and previous weights and their updates. Resulting weight update term is then related to the proximity measure used by Generalized Learning Vector Quantization classifiers. New algorithm and some major counterparts are tested and compared for synthetic and publicly available datasets. Results reveal that new classifier is faster in training and is more successful and robust in classifying test samples of datasets studied than the counterparts it is compared.


Classification; machine learning; supervised learning; learning vector quantization

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DOI: http://dx.doi.org/10.14311/NNW.2017.031


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