Neural Network Classification of Compound Mixtures

This paper demonstrates the usefulness of neural networks in classifying environmental samples from com-pound mixture data. This problem was solved by careful determination of neural network learning parameters and forward sequential selection of input features. Finally, the fundamental limit of any classifier on this data was determined using Bayes error bounding.