Appropriate Problems for Neural Network Learning

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4.3 When Should You Use Neural Networks?

The Backpropagation algorithm is the most commonly used ANN learning technique. It is appropriate for problems with the following six characteristics:

Characteristic 1: Instances Described by Many Attribute-Value Pairs

The target function is defined over instances described by a vector of predefined features — such as pixel values in an image.

Input attributes may be highly correlated or independent of one another
Input values can be any real number (not just binary or integer)

Example: In ALVINN, each instance is described by 960 pixel intensity values.
Counter-example: Symbolic logic problems where inputs are categorical labels are less naturally suited (though ANNs can still be applied).

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Appropriate Problems for Neural Network Learning

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4.3 When Should You Use Neural Networks?

Characteristic 1: Instances Described by Many Attribute-Value Pairs