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9

Worked Out Examples

In this final chapter, three worked out examples will be given of the
topics discussed in this book: classification, parameter estimation and
state estimation. They will take the form of a step-by-step analysis of
data sets obtained from real-world applications. The examples demon-
strate the techniques treated in the previous chapters. Furthermore, they
are meant to illustrate the standard approach to solving these types of
problems. Obviously, the MATLAB and PRTools algorithms as they were
presented in the previous chapters will be used. The data sets used here
are available through the website accompanying this book.

9.1 BOSTON HOUSING CLASSIFICATION PROBLEM

9.1.1 Data set description

The Boston Housing data set is often used to benchmark data analysis
tools. It was first described in Harrison and Rubinfield (1978). This paper
investigates which features are linked to the air pollution in several areas in
Boston. The data set can be downloaded from the UCI Machine Learning
repository at http://www.ics.uci.edu/~mlearn/MLRepository.html.
Each feature vector from the set contains 13 elements. Each feature
element provides specific information on an aspect of an area of a
suburb. Table 9.1 gives a short description of each feature element.

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