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Appendix B. CD Tools 309

Performing a K-NN classification with one neighbour and the "edit" approach,
the data is partitioned into two halves. A resubstitution classification method is
applied to the first half, which is classified with 10% error. Edition is then
performed by "discarding" the wrongly classified patterns. Finally, the second half
is classified using the edited first half. An overall test set error of 18% is obtained.

Author: JP Marques de Si, Engineering Faculty, Oporto University.

6.9 Perceptron

The Perceptron program has didactical purposes, showing how the training of a
linear discriminant using the perceptron learning rule progresses in a pattern-by-
pattern learning fashion for the case of separable and non-separable pattern
clusters.
The patterns are handwritten u's and v's drawn in an 8x7 grid. Two features
computed from these grids are used (see section 5.3). The user can choose either a
set of linearly separable patterns (set 1) or not (set 2).
Placing the cursor on each point displays the corresponding u or v.
Learning progresses by clicking the button "Step" or "Enter", in this case
allowing fast repetition.

Authors: JP Marques de S5, F Sousa, Engineering Faculty, Oporto University.

B.10 Syntactic Analysis

The SigParse program allows syntactic analysis experiments of signals to be
performed and has the following main functionalities:

- Linear piecewise approximation of a signal.
- Signal labelling.
- String parsing using a finite-state automaton.
Usually, operation with SigParse proceeds as follows:

1. Read in a signal from a text file, where each line is a signal value, up to a
maximum of 2000 signal samples. The signal is displayed in a picture box with
scroll, 4x zoom and sample increment ("step") facilities. The signal values are
also shown in a list box.
2. Derive a linear piecewise approximation of the signal, using the algorithm
described in section 6.1.1. The user specifies the approximation norm and a
deviation tolerance for the line segments. Good results are usually obtained
using the Chebychev norm. The piecewise linear approximation is displayed in
the picture box with black colour, superimposed on the original signal displayed