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- W2401849084 abstract "In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers." @default.
- W2401849084 created "2016-06-24" @default.
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- W2401849084 date "2015-08-25" @default.
- W2401849084 modified "2023-09-23" @default.
- W2401849084 title "Design of PCA-based pRBFNNs Pattern Classifier for Digit Recognition" @default.
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- W2401849084 doi "https://doi.org/10.5391/jkiis.2015.25.4.355" @default.
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