Supervised Learning for Operational Support

Abstract

Studies on machine learning have mainly been concerned with automatic learning from examples to develop the knowledge describing these examples. This is clearly different from the kind of learning as learning to ride a bicycle. In supervised learning, each example used is typically described by a number of attributes. The attributes are divided into inputs and outputs, and the learning process is to develop a model mapping the multiple inputs and outputs. The model is gradually refined during learning to minimise the errors between the predictions and real values of outputs, i.e., so-called supervised learning. The most widely studied supervised learning approach is the feedforward neural network (FFNN). The FFNN model and its application to process operational support will be introduced in this Chapter. The discussion on FFNN will be focused on many of the practical issues that have to be considered in applying FFNN. While the focus will be on FFNN, other supervised models will also be described and compared with FFNN. These include fuzzy FFNN, fuzzy set covering approach and fuzzy signed digraph.