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Ordinal Least Squares Support Vector Machines - A Discriminant Analysis Approach

机译：有序最小二乘支持向量机-判别分析方法

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This paper explores the extension of the classical ideas behind linear discriminant analysis to the problem of ordinal regression. It is shown how this reasoning fits in a framework of least squares support vector machines (LS-SVMs) and kernel machines, h

机译：本文探讨了线性判别分析背后的经典思想对序数回归问题的扩展。显示了这种推理如何适合于最小二乘支持向量机（LS-SVM）和内核机

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来源
《Machine Learning for Signal Processing, 2006 IEEE Workshop on; Maynooth,Ireland 》||P.247-252|共6页
会议地点 MaynoothIreland
作者
Pelckmans; K.; Karsmakers; P.; Suykens; J.A.K.; De Moor; B.;
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原文格式 PDF
正文语种 eng
中图分类工业技术 ;
关键词
least squares approximations; regression analysis; support vector machines; LS-SVM; kernel machine; least squares support vector machines; linear discriminant analysis; maximal margin; nonlinear extension; ordinal regression;

机译：最小二乘近似;回归分析;支持向量机; LS-SVM;核机;最小二乘支持向量机;线性判别分析;最大余量;非线性扩展;有序回归;

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机译：基于线性判别分析的正向选择稀疏最小二乘支持向量机

Ordinal Least Squares Support Vector Machines - A Discriminant Analysis Approach

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