The aim of the book, Machine Learning, Neural and Statistical Classification, is to review the different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
A wide variety of approaches has been considered in this task. Three main historical strands of research can be identified: statistical, machine learning and neural network. These have largely involved different professional and academic groups, and emphasised different issues. All groups have, however, had some objectives in common. They have all attempted to derive procedures that would be able:
a) to equal, if not exceed, a human decision-maker's behaviour, but have the advantage of consistency and, to a variable extent, explicitness,
b) to handle a wide variety of problems and, given enough data, to be extremely general,
c) to be used in practical settings with proven success.