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

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KNOWLEDGE ENGINEERING AND DATA MINING
Figure 9.22
examples
Performance evaluation of the digit recognition network trained with ‘noisy’
handwritten) document and load it into their computer as an editable file.
Handwritten digit recognition systems are widely used in processing zip codes
on mail envelopes (LeCun et al., 1990).
Case study 5: Prediction neural networks
I want to develop an intelligent system for real-estate appraisal. Will a
neural network work for this problem?
Real-estate appraisal is a problem of predicting the market value of a given house
based on the knowledge of the sales prices of similar houses. As we mentioned
earlier, this problem can be solved with expert systems as well as neural
networks. Of course, if we choose to apply a neural network, we will not be able
to understand how an appraisal of a particular house is reached – a neural
network is essentially a black-box to the user and rules cannot be easily extracted
from it. On the other hand, an accurate appraisal is often more important than
understanding how it was done.
In this problem, the inputs (the house location, living area, number of
bedrooms, number of bathrooms, land size, type of heating system, etc.) are
well-defined, and normally even standardised for sharing the housing market
information between different real estate agencies. The output is also well
defined – we know what we are trying to predict. Most importantly, there are
many examples we can use for training the neural network. These examples
are the features of recently sold houses and their sales prices.