What have neural networks to offer statistical pattern processing?
David Lowe · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
The usefulness of algorithmic `neural' networks applied to signal and statistical pattern processing is considered in the context of more traditional techniques. The ability of networks to perform well in a variety of tasks is linked to the networks' flexibility in being able to incorporate the functionality of more traditional, established techniques. Discussions and examples of these relationships are presented, including density estimation, functional interpolation, clustering, and discriminant analysis. It is argued that the current embodiments of neural network structures are useful pattern analysis tools precisely because they provide an interpretational `glue' or a framework which links together a variety of methods and viewpoints, thus constituting a generic form of pattern analysis methods. This also indicates their limits.