Polynomial expansion of the binary classification function

TL;DR

This paper introduces a new polynomial expansion method for binary classification that efficiently approximates regression functions, providing a fast, robust, and overfitting-resistant classification approach especially suited for multi-dimensional data.

Contribution

It presents a novel, simple derivation for polynomial coefficient approximation tailored for multi-dimensional classification tasks.

Findings

Fast and robust classification technique demonstrated.

Resistant to over-fitting in high-dimensional settings.

Applicable to multi-dimensional classification problems.

Abstract

This paper describes a novel method to approximate the polynomial coefficients of regression functions, with particular interest on multi-dimensional classification. The derivation is simple, and offers a fast, robust classification technique that is resistant to over-fitting.