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On the small sample behavior of the class-sensitive neural network

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2 Author(s)
Chen, C.H. ; Dept. of Electr. & Comput. Eng., Massachusetts Univ., North Dartmouth, MA, USA ; Jozwik, A.

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

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996