Page 5 -

. . .
VI~I Preface

checking the presented results should not constitute a major difficulty. The CD also
includes a set of complementary software tools for those topics where the
availability of such tools is definitely a problem. Therefore, from the beginning of
the book, the reader should be able to follow the taught methods with the guidance
of practical applications, without having to do any programming, and concentrate
solely on the correct application of the learned concepts.
The main organization of the book is quite classical. Chapter 1 presents the
basic notions of pattern recognition, including the three main approaches
(statistical, neural networks and structural) and important practical issues. Chapter
2 discusses the discrimination of patterns with decision functions and
representation issues in the feature space. Chapter 3 describes data clustering and
dimensional reduction techniques. Chapter 4 explains the statistical-based methods,
either using distribution models or not. The feature selection and classifier
evaluation topics are also explained. Chapter 5 describes the neural network
approach and presents its main paradigms. The network evaluation and complexity
issues deserve special attention, both in classification and in regression tasks.
Chapter 6 explains the structural analysis methods, including both syntactic and
non-syntactic approaches. Description of the datasets and the software tools
included in the CD are presented in Appendices A and B.
Links among the several topics inside each chapter, as well as across chapters,
are clarified whenever appropriate, and more recent topics, such as support vector
machines, data mining and the use of neural networks in structural matching, are
included. Also, topics with great practical importance, such as the dimensionality
ratio issue, are presented in detail and with reference to recent findings.
All pattern recognition methods described in the book start with a presentation
of the concepts involved. These are clarified with simple examples and adequate
illustrations. The mathematics involved in the concepts and the description of the
methods is explained with a concern for keeping the notation cluttering to a
minimum and using a consistent symbology. When the methods have been
sufficiently explained, they are applied to real-life data in order to obtain the
needed grasp of the important practical issues.
Starting with chapter 2, every chapter includes a set of exercises at the end. A
large proportion of these exercises use the datasets supplied with the book, and
constitute computer experiments typical of a pattern recognition design task. Other
exercises are intended to broaden the understanding of the presented examples,
testing the level of the reader's comprehension.
Some background in probability and statistics, linear algebra and discrete
mathematics is needed for full understanding of the taught matters. In particular,
concerning statistics, it is assumed that the reader is conversant with the main
concepts and methods involved in statistical inference tests.
All chapters include a list of bibliographic references that support all
explanations presented and constitute, in some cases, pointers for further reading.
References to background subjects are also included, namely in the area of
statistics.
The CD datasets and tools are for the Microsoft Windows system (95 and
beyond). Many of these datasets and tools are developed in Microsoft Excel and it
should not be a problem to run them in any of the Microsoft Windows versions.