On non-iterative training of a neural classifier

TL;DR

This paper introduces two non-iterative, deterministic methods for neural network training based on a new algorithm that separates points in high-dimensional space, demonstrating their effectiveness on the IRIS dataset and highlighting their potential for deep learning.

Contribution

The paper presents a novel non-iterative, deterministic approach for training neural classifiers using a new point-separation algorithm, potentially replacing traditional iterative methods.

Findings

Methods successfully applied to IRIS dataset

Achieved efficient and fast classification

Potential to supersede iterative training methods

Abstract

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.