AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

WILL A NEURAL NETWORK WORK FOR MY PROBLEM?
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Choosing training examples is critical for an accurate prediction. A training
set must cover the full range of values for all inputs. Thus, in the training set for
real estate appraisal, we should include houses that are large and small,
expensive and inexpensive, with and without garages, etc. And the training set
has to be sufficiently large.
But how do we determine when the size of a training set is ‘sufficiently
large’?
A network’s ability to generalise is influenced by three main factors: the size of
the training set, the architecture of the network, and the complexity of the
problem. Once the network architecture is decided, the issue of generalisation is
resolved by the adequacy of the training set. An appropriate number of training
examples can be estimated with Widrow’s rule of thumb, which suggests that,
for a good generalisation, we need to satisfy the following condition (Widrow
and Stearns, 1985; Haykin, 1999):
N ¼ n w
e ;
ð9:1Þ
where N is the number of training examples, n w is the number of synaptic
weights in the network, and e is the network error permitted on test.
Thus, if we allow an error of, say, 10 per cent, the number of training
examples should be approximately 10 times bigger than the number of weights
in the network.
In solving prediction problems, including real-estate appraisal, we often
combine input features of different types. Some features, such as the house’s
condition and its location, can be arbitrarily rated from 1 (least appealing) to 10
(most appealing). Some features, such as the living area, land size and sales price,
are measured in actual physical quantities – square metres, dollars, etc. Some
features represent counts (number of bedrooms, number of bathrooms, etc.), and
some are categories (type of heating system).
A neural network works best when all its inputs and outputs vary within the
range between 0 and 1, and thus all the data must be massaged before we can use
them in a neural network model.
How do we massage the data?
Data can be divided into three main types: continuous, discrete and categorical
(Berry and Linoff, 1997), and we normally use different techniques to massage
different types of data.
Continuous data vary between two pre-set values – minimum and maximum,
and can be easily mapped, or massaged, to the range between 0 and 1 as:
actual value minimum value
massaged value ¼
maximum value minimum value
ð9:2Þ