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6 1 Basic Notions

outside its scope. As for the greenish orange its feature vector is nearly at equal
distance from both prototypes and its classification is problematic. If we use,
instead of the Euclidian distance, another distance measure that weighs more
heavily vertical deviations than horizontal deviations, the greenish orange would
also be wrongly classified.
In general practice pattern classification systems are not flawless and we may
expect errors due to several causes:
- The features used are inadequate or insufficient. For instance, the classification
of the problematic greenish orange would probably improve by using an
additional texture feature measuring the degree of surface roughness.
- The pattern samples used to design the classifier are not sufficiently
representative. For instance, if our intention is to discriminate apples from
oranges we should have to include in the apples sample a representative variety
of apples, including the red ones as well.
- The classifier is not efficient enough in separating the classes. For instance, an
inefficient distance measure or inadequate prototypes are being used.
- There is an intrinsic overlap of the classes that no classifier can resolve.

In this book we will focus our attention on the aspects that relate to the selection
of adequate features and to the design of efficient classifiers. Concerning the initial
choice of features it is worth noting that this is more an art than a science and, as
with any art, it is improved by experimentation and practice. Besides the
appropriate choice of features and similarity measures, there are also other aspects
responsible for the high degree of classifying accuracy in humans. Aspects such as
the use of contextual information and advanced knowledge structures fall mainly in
the domain of an artificial intelligence course and will be not dealt with in this
book. Even the human recognition of objects is not always flawless and contextual
information risks classifying a greenish orange as a lemon if it lies in a basket with
lemons.

1.2.2 Regression Tasks

We consider now another type of task, directly related to the cognitive inference
process. We observe such a process when animals start a migration based on
climate changes and physiological changes of their internal biological cycles. In
daily life, inference is an important tool since it guides decision optimisation. Well-
known examples are, for instance, keeping the right distance from the vehicle
driving ahead in a road, forecasting weather conditions, predicting firm revenue of
investment and assessing loan granting based on economic variables.
Let us consider an example consisting of forecasting firm A share value in the
stock exchange market, based on past information about: the share values of firm A
and of other firms; the currency exchange rates; the interest rate. In this situation
we want to predict the value of a variable based on a sequence of past values of the
same and other variables, which in the one-day forecast situation of Figure 1.5 are:
r,, r~, rc, Euro-USD rate, Interest rate for 6 months.