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- W2141967631 abstract "The behavior of a neural network when the number of training samples is small is examined by using a large remote-sensing database. The paper also presents a new way to reduce the size of the training set without significantly decreasing the classification quality. The effectiveness of the proposed algorithm is examined on the class-sensitive neural network (CSNN) which is known to have a superior classification accuracy over the standard backpropagation trained neural network. It is shown that with a combination of the sample set condensation algorithm and the CSNN, the classification performance degrades only slightly even when the number of training samples is quite small." @default.
- W2141967631 created "2016-06-24" @default.
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- W2141967631 date "1996-01-01" @default.
- W2141967631 modified "2023-10-05" @default.
- W2141967631 title "On the small sample behavior of the class-sensitive neural network" @default.
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- W2141967631 doi "https://doi.org/10.1109/icpr.1996.547417" @default.
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