交叉验证,引导和支持向量机

M. Tsujitani, Yusuke Tanaka
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引用次数: 16

摘要

本文研究了重采样方法在支持向量机中的应用。在确定最优调整参数和自举偏差时,我们考虑了留一交叉验证(CV),以便总结支持向量机的拟合优度度量。为了在用训练样本构建的预测规则中提供对过量误差偏差的估计,还采用了留一CV。我们分析了鲭鱼卵调查和肝脏疾病研究的数据。
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Cross-Validation, Bootstrap, and Support Vector Machines
This paper considers the applications of resampling methods to support vector machines (SVMs). We take into account the leaving-one-out cross-validation (CV) when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.
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