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).