尽管在这篇文章中包括了E4XServlet和一个可以在下载中找到的样本压缩包的例子，但是我们着重介绍使用Axis。
While we have included our E4XServlet and one example in the samples zip, found under Download, we will mainly focus on using Axis in this article.
结果通过在确认样本上计算得到压缩因子和校正预后指数，可改善预测。
Results Prediction was improved by shrinkage factor and adjusted prognostic index based on validation sample.
主成分分析法可以提取样本集的主元，从而降低样本的维数，甚至可以实现样本的最优压缩。
Principal component analysis method can reduce dimensions of samples and even produce optimal compression of samples.
主成分分析法可以提取样本集的主元，从而降低样本的维数，甚至可以实现样本的最优压缩。
Principal component analysis method can reduce dimensions of samples and even produce optimal compression of samples.
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