A hybrid kernel principal component analysis and support vector machine model for analysing sonographic features of parotid glands in Sjogren's syndrome

DOI10.1504/IJMMNO.2011.037202zbMath1205.92036OpenAlexW2042601857MaRDI QIDQ622830

Ping-Feng Pai, Ming-Fu Hsu, Hsin-Hua Chen, Ya-Hsin Chang, Ja-Chih Fu

Publication date: 4 February 2011

Published in: International Journal of Mathematical Modelling and Numerical Optimisation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1504/ijmmno.2011.037202

zbMATH Keywords

principal component analysismedical diagnosissupport vector machinesSVMultrasoundkernel PCAKPCAchronic inflammatory diseaseexocrine glandsimmune algorithmsparotid glandsSjogren's syndromesonography

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

Related Items (1)

An Enhanced Support Vector Machines Model for Classification and Rule Generation

This page was built for publication: A hybrid kernel principal component analysis and support vector machine model for analysing sonographic features of parotid glands in Sjogren's syndrome