On non-iterative training of a neural classifier part-II: Clustering of points and their classification using an NN architecture

摘要

As described in Part I of this two paper series, an algorithm was recently discovered, which separates points in n-dimension by planes in such a manner that no two points are left un-separated by at least one plane. By using this new algorithm it can be shown that there are two ways of classification by a neural network, for a large dimension feature space, both of which are non-iterative and deterministic and one could apply both these methods to a classical pattern recognition problem and present the results. It is expected these methods will now be widely used for the training of neural networks for Deep Learning not only because of their non-iterative and deterministic nature but also because of their efficiency and speed and that they may supersede other classification methods which are iterative in nature and rely on error minimization.