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

Source: [Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification](<https://www.sota2.com/research/paper/68fa688a1e209131cea1ccb0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:39:59.706Z

Authors: W. H. Huang

Published: 2020-10-16T01:05:49.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.08087>)

[PDF](<https://arxiv.org/pdf/2010.08087.pdf>)