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20 I Basic Notions

Concerning software for specific PR methodologies it is worth mentioning the
impressive number of software products and tools in the Neural Network and Data
Mining areas. In chapter 5 we will use one such tool, namely the Support Vector
Machine Toolbox for Matlub, developed by S.R. Gunn at the University of
Southampton.
Unfortunately there are practically no software tools for structural PR, except
for a few non user-friendly parsers. There is also a lack of tools for guiding
important project decisions such as the choice of a reasonable dinlensionality ratio.
The CD offered with this book is intended to fill in the gaps of available software,
supplying the necessary tools in those topics where none exist (or are not readily
available).
All the main PR approaches and techniques described in the book are illustrated
with applications to real-life problems. The corresponding datasets, described in
Appendix A, are also supplied in the CD. At the end of the following chapters
several exercises are proposed, many involving computer experiments with the
supplied datasets.
With Statistics, Matlab, SPSS or any other equivalent product and the
complementary tools of the included CD the reader is encouraged to follow the
next chapters in a hands-on fashion, trying the presented examples and freeing
hidher imagination.

Bibliography

Bishop CM (I 995) Neural Networks for Pattern Recognition. Clarendon Press, Oxford.
Cherkassky V, Mulier F (1998) Learning from Data. John Wiley & Sons, Inc.
Duda RO, Hart PE (1973) Pattern Classificalion and Scene Analysis. Wiley, New York.
Friedrnan M, Kandel A (1999) lntroduclion to Paltern Recognition. World Scientific,
Irnperial College Press.
Fu KS (1977) Introduction to Syntaclic Pattern Recognition. In: Fu KS (ed) Syntactic
Pattern Recognition, Applications. Springer-Verlag, Berlin, Heidelberg.
Hartigan, JA (1975) Clustering algorilhrns. Wiley, New York.
Jain AK, Duin RPW, Mao J (2000). Statistical Pattern Recognition: A Review. IEEE Tr Patt
An Mach Intel 1:4-37.
Mitchell TM (1997) Machine Learning. McGraw Hill Book Co., Singapore.
Schalkoff R (1992) Pattern Recognition. Wiley.
Schiirrnann J (1996) Pattern Classification. A Unified View of Statistical and Neural
Approaches. John Wiley & Sons, Inc.
Simon JC. Backer E, Sallentin J (1982) A Unifying Viewpoint on Pattern Recognition. In:
Krishnaiah PR and Kanal LN (eds) Handbook of Statistics vo1.2, North Holland Pub.
Co., 45 1-477.