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Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB by David M. J. Tax, Dick de Ridder, Ferdinand van der Heijden, Robert Duin

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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 demonstrate 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.

Table 9.1 The features of the Boston Housing data set

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The goal we set for this section is to predict whether the median price ...

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