Analytics Insight

Machine Learning, Stanford University

Stanford University’s Machine Learning (XCS229) is a 100% online, instructor-led course offered by the Stanford School of ...
Scientific Research Publishing

Proposal for a Machine Learning Model to Improve Hauling Productivity in an Open-Pit Mine ()

This proposal outlines a machine learning-based approach aimed at improving productivity in haulage operations within ...
Analytics Insight

10 Must-Know Machine Learning Algorithms for Data Science in 2026

Machine learning algorithms help computers analyse large datasets and make accurate predictions automatically.Classic models ...
IEEE

Handwritten Digit Classification Using Support Vector Machine Algorithm Comparing with Logistic Regression for Improving Accuracy

Abstract: The work's objective is to assess how well the classification techniques of logistic regression and support vector machine predict handwritten digits. A Digit dataset with 985 records is ...
Scientific Research Publishing

Brereton, R.G. and Lloyd, G.R. (2010) Support Vector Machines for Classification and Regression. The Analyst, 135, 230-267.

ABSTRACT: Support vector regression (SVR) and computational fluid dynamics (CFD) techniques are applied to predict the performance of an automotive torque converter in the design process of turbine ...
Frontiers

Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models

Our findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may ...
IEEE

Support Vector Machine Regression Optimised by Genetic Algorithms for Calculating the Ageing Population

Abstract: The aging problem has an increasing impact on China's economy, employment, and medical care, so it is important to make accurate predictions of the aging population. In this paper, we use ...
Frontiers

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f.