Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification

TL;DR

This paper evaluates a low-cost, homogeneous machine learning algorithm for image classification, focusing on its performance and potential integration into cloud applications, emphasizing simplicity and efficiency.

Contribution

It introduces and assesses a novel, cost-effective homogeneous model algorithm based on conditional probability theories for image classification.

Findings

Competitive performance with state-of-the-art models

Seamless integration into cloud-based systems

Reduced computational costs

Abstract

The image classification machine learning model was trained with the intention to predict the category of the input image. While multiple state-of-the-art ensemble model methodologies are openly available, this paper evaluates the performance of a low-cost, simple algorithm that would integrate seamlessly into modern production-grade cloud-based applications. The homogeneous models, trained with the full instead of subsets of data, contains varying hyper-parameters and neural layers from one another. These models' inferences will be processed by the new algorithm, which is loosely based on conditional probability theories. The final output will be evaluated.