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Concerning the flexibility of PR design software, there is a broad spectrum of
possibilities. At one end we find "closed" products where the user can only
perform menu operations. At the other end we find "open" products allowing the
user to program any arbitrarily complex PR algorithm. A popular example of such
a product is Marlab from The Marh Works, Inc., a mathematical software product.
Designing a PR application in Marlab gives the user the complete freedom to
implement specific algorithms and perform complex operations, namely using the
routines available in the Marlab Toolboxes. For instance, one can couple routines
from the Neural Networks Toolbox with routines from the Image Processing
Toolbox in order to develop image classification applications. The penalty to be
paid for this flexibility is that the user must learn to program in the Matlab
language, with non-trivial language learning and algorithm development times.
Some statistical software packages incorporate relevant tools for PR design.
Given their importance and wide popularisation two of these products are worth
mentioning: SPSS from SPSS Inc. and Siatistica from StatSoft Inc. Both products
require minimal time for familiarization and allow the user to easily perform
classification and regression tasks using a scroll-sheet based philosophy for
operating with the data. Figure 1.14 illustrates the Statistics scroll-sheet for a cork
stoppers classification problem with colunln C filled in with numeric codes of the
supervised class labels and the other columns (ART to PRT) filled in with feature
values. Concerning flexibility, both SPSS and Siaristica provide macro
constructions. As a matter of fact Siatistico is somewhere between a "closed" type
prnduc~ and Matlab, since it provides programn~ing facilities such as the use of
external code (DLLs) and application programming interfaces (API). In this book
we will extensively use the Statisrica (kernel release 5.5A for Windows), with a
few exceptions, for illustrating PR methods with appropriate examples and real
data.

8 .-
, r:! A
-
iYALue A +
NU~V~
3 4 5 6 7 8 9-
AiT I N I PRT I ARM I PRM IARTGI NG /PRI
i48 1 117 76 492 2 46 6 47 33 0 3 0 40

Figure 1.14. S~a~isrica scroll-sheet for the cork stoppers data. Each row
corresponds to a pattern. C is the class label column. The other columns
correspond to the features.