A New Oscillating-Error Technique for Classifiers

TL;DR

This paper introduces an oscillating-error technique for classifiers that adjusts errors dynamically, allowing for flexible convergence and improved classification performance on benchmark datasets.

Contribution

It presents a novel error correction method that enables oscillating adjustments, enhancing classifier flexibility and convergence speed compared to traditional approaches.

Findings

Successfully classifies benchmark datasets

Reduces training times in batch mode

Can be integrated into neural network architectures

Abstract

This paper describes a new method for reducing the error in a classifier. It uses an error correction update that includes the very simple rule of either adding or subtracting the error adjustment, based on whether the variable value is currently larger or smaller than the desired value. While a traditional neuron would sum the inputs together and then apply a function to the total, this new method can change the function decision for each input value. This gives added flexibility to the convergence procedure, where through a series of transpositions, variables that are far away can continue towards the desired value, whereas variables that are originally much closer can oscillate from one side to the other. Tests show that the method can successfully classify some benchmark datasets. It can also work in a batch mode, with reduced training times and can be used as part of a neural network architecture. Some comparisons with an earlier wave shape paper are also made.