Page 150 - Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

5

Supervised Learning

One method for the development of a classifier or an estimator is the
so-called model-based approach. Here, the required availability of the
conditional probability densities and the prior probabilities are obtained
by means of general knowledge of the physical process and the sensory
system in terms of mathematical models. The development of the esti-
mators for the backscattering coefficient, discussed in Chapter 3, follows
such an approach.
In many other applications, modelling the process is very difficult if
not impossible. For instance, in the mechanical parts application,
discussed in Chapter 2, the visual appearance of the objects depends
on many factors that are difficult to model. The alternative to the
model-based approach is the learning from examples paradigm. Here,
it is assumed that in a given application a population of objects is
available. From this population, some objects are selected. These
selected objects are called the samples. Each sample is presented to
the sensory system which returns the measurement vector associated
with that sample. The purpose of learning (or training) is to use these
measurement vectors of the samples to build a classifier or an estima-
tor.
The problem of learning has two versions: supervised and unsuper-
vised, that is, with or without knowing the true class/parameter of the
sample. See Figure 5.1. This chapter addresses the first version. Chapter 7
deals with unsupervised learning.

Classification, Parameter Estimation and State Estimation: An Engineering Approach using MATLAB
F. van der Heijden, R.P.W. Duin, D. de Ridder and D.M.J. Tax
Ó 2004 John Wiley & Sons, Ltd ISBN: 0-470-09013-8