About kernel latent variable approaches and SVM
About kernel latent variable approaches and SVM
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DOI：
10.1002/cem.937
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发表时间：
2005-05-01
影响因子：
2.4
通讯作者：
Walczak, B
中科院分区：
文献类型：
--
作者：
Czekaj, T;Wu, W;Walczak, B
本文的目的是证明，核隐变量方法具有与基于正则化的核方法集（例如支持向量机）相当的预测能力。核潜变量方法是核岭回归的替代方法，就像PCR或PLS是岭回归的替代方法一样。模拟数据集和微阵列数据集证明了这些方法的性能。版权所有（C）2006约翰威利父子有限公司
The aim of this paper is to demonstrate, that kernel latent variables approaches have a comparable predictive power with the set of kernel approaches based on regularization (e.g. Support Vector Machines). Kernel latent variable approaches are an alternative to kernel ridge regression, in the same way as PCR or PLS are the alternative approaches to Ridge Regression. Performance of these approaches is demonstrated for simulated data sets and microarray data set. Copyright (C) 2006 John Wiley & Sons, Ltd.