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- W2137750563 abstract "Recently, a feature extraction method based on decision boundary has been proposed for neural networks. The method is based on the fact that the vector normal to the decision boundary contains information useful for discriminating between classes. However, the normal vector was estimated numerically, resulting in inaccurate estimation and a long computational time. The authors propose a new method to calculate the normal vector analytically and derive all the necessary equations for 3 layer feedforward neural networks with a sigmoid function. Experiments show that the proposed method provides a noticeably improved performance." @default.
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- W2137750563 date "2002-11-07" @default.
- W2137750563 modified "2023-09-27" @default.
- W2137750563 title "Analytical decision boundary feature extraction for neural networks" @default.
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- W2137750563 doi "https://doi.org/10.1109/igarss.2000.860340" @default.
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