• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Bagging for robust non-linear multivariate calibration of spectroscopy
【24h】

Bagging for robust non-linear multivariate calibration of spectroscopy

机译：套袋用于稳健的非线性多元光谱校正

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents the application of the bagging technique for non-linear regression models to obtain more accurate and robust calibration of spectroscopy. Bagging refers to the combination of multiple models obtained by bootstrap re-sampling with replacement into an ensemble model to reduce prediction errors. It is well suited to "non-robust" models, such as the non-linear calibration methods of artificial neural network (ANN) and Gaussian process regression (GPR), in which small changes in data or model parameters can result in significant change in model predictions. A specific variant of bagging, based on sub-sampling without replacement and named subagging, is also investigated, since it has been reported to possess similar prediction capability to bagging but requires less computation. However, this work shows that the calibration performance of subagging is sensitive to the amount of sub-sampled data, which needs to be determined by computationally intensive cross-validation. Therefore, we suggest that bagging is preferred to subagging in practice. Application study on two near infrared datasets demonstrates the effectiveness of the presented approach.
机译：本文介绍了套袋技术在非线性回归模型中的应用，以获得更准确，更可靠的光谱校准。套袋是指通过自举重新采样获得的多个模型的组合，并替换为整体模型以减少预测误差。它非常适合“非健壮”模型，例如人工神经网络（ANN）和高斯过程回归（GPR）的非线性校准方法，其中数据或模型参数的微小变化可能会导致模型的显着变化。模型预测。还研究了一种基于装袋的特定变体，该变种基于无需替换的子采样并命名为子装袋，因为据报道它具有与装袋相似的预测能力，但需要较少的计算。但是，这项工作表明，子校准的校准性能对子采样数据量很敏感，这需要通过计算量大的交叉验证来确定。因此，在实践中，我们建议套袋优先于细分。对两个近红外数据集的应用研究证明了该方法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号