A gradient descent boosting spectrum modeling method based on back interval partial least squares
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- 作者：Dong RenaAuthor Vitae ; Fangfang QuaAuthor Vitae ; Ke Lva ; luk@ucas.ac.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Zhong ZhangaAuthor Vitae ; Honglei XubAuthor Vitae ; Xiangyu WangcAuthor Vitae
- 关键词：Near-infrared spectroscopy ; Gradient descent ; GD-Boosting-BiPLS ; Variable selection ; Parameter fusion
- 刊名：Neurocomputing
- 出版年：2016
- 出版时间：1 January 2016
- 年：2016
- 卷：171
- 期：Complete
- 页码：1038-1046
- 全文大小：1782 K
文摘
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An ensemble model of gradient descent boosting and BiPLS is proposed.
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With BiPLS as the base model method can reduce the sensitivity of boosting to noise.
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The gradient descent boosting strategy can improve the performance of base models.
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The iteration parameter and the segmentation parameter are fused to simplify the model.
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The final ensemble model can remain stable at different initial number of iterations.