Holographic neural networks as nonlinear discriminants for chemical applications

A holographic neural network has been investigated for use as a discriminant. Six sets of artificial data and two data sets of infrared spectra, reduced using principal component analysis, of prepared cervical smears were analyzed by four regular discriminant methods as well as by the holographic neural network method. In all cases, it was found that the holographic neural network method gave comparable, and in some cases superior, results to the other discriminant methods. The holographic neural network method is simple to apply and has the advantage that it can be easily refined when new data become available without disturbing the original mapping. It is suggested that the holographic neural network method should be seriously considered when discrimination methods need to be applied.