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- W1557446017 abstract "The paper presents an input-expansion-based improved method for general regression neural network (GRNN) and BP network. Using second-order inner product function or Chebyshev polynomial function to expand input vector of original samples, which makes input vector mapped into a higher-dimension pattern space and thus leads to the samples data more easily separable. The classification results for both Iris data and remote sensing data show that general regression neural network is superior to Levenberg-Marquardt BP network (LMBPN) and moreover input-expansion method may efficiently enhance classification accuracy for neural network models." @default.
- W1557446017 created "2016-06-24" @default.
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- W1557446017 date "2006-01-01" @default.
- W1557446017 modified "2023-09-27" @default.
- W1557446017 title "Comparative Study on Input-Expansion-Based Improved General Regression Neural Network and Levenberg-Marquardt BP Network" @default.
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- W1557446017 doi "https://doi.org/10.1007/11816157_9" @default.
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