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 ...
Scientific Research Publishing

Support Vector Regression Methodology for Performance Prediction of an Automotive Torque Converter with Turbine Parametric Model ()

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 geometry. A ...
Visual Studio Magazine

Linear Support Vector Regression Using C# with Particle Swarm Training

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...
Visual Studio Magazine

Linear Support Vector Regression from Scratch Using C# with Evolutionary Training

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...
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

Research on the Liquor Yield Prediction Model Integrating Hybrid Kernel Support Vector Regression and Dung Beetle Optimizer

Abstract: To enhance the efficiency of liquor production and reduce subjective errors caused by manual operations, this study explores the relationship between production process parameters and yield ...
Frontiers

SABO-ILSTSVR: a genomic prediction method based on improved least squares twin support vector regression

In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, ...
IEEE

Novel Multi-output Support Vector Regression Model via Double Regularization

Abstract: Multi-output regression estimation aims at mining a vector-valued function from multi-dimensional input vector to multi-dimensional output vector. However, the output variables may be ...