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- W2070014925 abstract "The performance accuracy of the Artificial Neural Network (ANN) is highly dependent on the class distribution. Data multi-randomization before classification is proposed in this paper in order to obtain a proper classification model, which guaranties well performance of the classifiers. Multi randomization aims to allocate the best class distribution by re-ordering the input dataset randomly. In this paper, Learning Vector Quantization (LVQ) which is a supervised ANN, Multilayer perceptron (MLP), unsupervised Self organizing Map (SOM) and Radial Base Function (RBF) are used to classify multi randomized brain Magnetic Resonance Imaging (MRI) dataset. The proposed method showed significant improvement in the stability of the classifiers." @default.
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- W2070014925 date "2013-02-01" @default.
- W2070014925 modified "2023-09-27" @default.
- W2070014925 title "Brain imaging classification based On Learning Vector Quantization" @default.
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- W2070014925 doi "https://doi.org/10.1109/iccspa.2013.6487253" @default.
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