To simulate intelligent behavior the abilities of memorization and generalization are essential. These are basic properties of artificial neural networks. The following definitions are according to the Collins English Dictionary:
|to memorize:||to commit to memory; learn so as to remember.|
|to generalize:||to form general principles or conclusions from detailed facts, experience, etc.|
Memorizing, given facts, is an obvious task in learning. This can be done by storing the input samples explicitly, or by identifying the concept behind the input data, and memorizing their general rules.
The ability to identify the rules, to generalize, allows the system to make predictions on unknown data.
Despite the strictly logical invalidity of this approach, the process of reasoning from specific samples to the general case can be observed in human learning.
Generalization also removes the need to store a large number of input samples. Features common to the whole class need not to be repeated for each sample - instead the system needs only to remember which features are part of the sample. This can dramatically reduce the amount of memory needed, and produce a very efficient method of memorization.