C++ Neural Networks and Fuzzy Logic: Preface

Generalization Ability

The analogy for a neural network presented at the beginning of the chapter was that of a multidimensional

mapping surface that maps inputs to outputs. For each unseen input with respect to a training set, the

generalization ability of a network determines how well the mapping surface renders the new input in the

output space. A stock market forecaster must generalize well, otherwise you lose money in unseen market

conditions. The opposite of generalization is memorization. A pattern recognition system for images of

handwriting, should be able to generalize a letter A that is handwritten in several different ways by different

people. If the system memorizes, then you will not recognize the letter A in all cases, but instead will

categorize each letter A variation separately. The trick to achieve generalization is in network architecture,

design, and training methodology. You do not want to overtrain your neural network on expected outcomes,

but rather should accept a slightly worse than minimum error on your training set data. You will learn more

about generalization in Chapter 14.

Summary

Learning and training are important issues in applying neural networks. Two broad categories of network

learning are supervised and unsupervised learning. Supervised learning provides example outputs to compare

to while unsupervised learning does not. During supervised training, external prototypes are used as target

outputs and the network is given a learning algorithm to follow and calculate new connection weights that

bring the output closer to the target output. You can refer to networks using unsupervised learning as

self−organizing networks, since no external information or guidance is used in learning. Several neural

network paradigms were presented in this chapter along with their learning and training characteristics.

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C++ Neural Networks and Fuzzy Logic:Preface

Generalization Ability

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