On non-iterative training of a neural classifier
This work addresses the need for faster and more reliable neural network training methods in deep learning, though it appears incremental as it builds on an existing separation algorithm.
The paper tackles the problem of iterative and error-minimization-based neural network training by introducing two non-iterative, deterministic classification methods for high-dimensional feature spaces, demonstrating their effectiveness on the IRIS dataset with implied efficiency and speed gains.
Recently an algorithm, was 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{[}1-3{]}. By using this new algorithm we show 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. To demonstrate the power of both these methods we apply them exhaustively to the classical pattern recognition problem: The Fisher-Anderson's, IRIS flower data set 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 will supersede other classification methods which are iterative in nature and rely on error minimization.