On the small sample behavior of the class-sensitive neural network
C.H. Chen, Adam Jóźwik · 1996
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.