Variable selection by rank-one updates for least squares support vector machines

机译：最小二乘支持向量机的秩更新更新变量选择

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Least squares support vector machines (LS-SVM) classifiers are a class of simple, yet powerful, kernel methods whose solution follows from a set of linear equations. Here, forward and backward algorithms, based on this technique, are proposed for fast and

机译：最小二乘支持向量机（LS-SVM）分类器是一类简单但功能强大的核方法，其解决方案来自一组线性方程。在此，基于此技术提出了前进和后退算法，以实现快速

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《Neural Networks, The 2007 IEEE International Joint Conference on》|2007年|P.2283-2288|共6页
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Ojeda; F.; Suykens; J.A.K.; De Moor; B.;
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中图分类工业技术;
关键词
least squares approximations; matrix algebra; support vector machines; LOO; LS-SVM classifier; forward-backward algorithm; gene selection; kernel matrix; least squares support vector machine; leave-one-out estimator; linear equation; microarray data;

机译：最小二乘近似;矩阵代数;支持向量机; LOO; LS-SVM分类器;前向后退算法;基因选择;核矩阵;最小二乘支持向量机;留一法估计器;线性方程;微阵列数据;

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4. Variable selection by rank-one updates for least squares support vector machines [C] . Ojeda, F., Suykens, Neural Networks, The 2007 IEEE International Joint Conference on . 2007

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Variable selection by rank-one updates for least squares support vector machines

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