Constrained neural network learning algorithm for estimating advertising effectiveness

This paper proposes a neural network algorithm for estimating advertising effectiveness. The basic premise under this algorithm is that the relationship between input and output monotonically increases. The algorithm is useful for domains such as estimation of advertising effectiveness, for which only deficient data are available. The experimental simulation shows that the algorithm builds a reasonable model for estimating advertising effectiveness. © 1998 Scripta Techinca, Electr Eng Jpn, 126(1): 34–41, 1999