.png){kind=icon}
返回
Multivariate data modeling using modified kernel partial least squares
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cherd.2014.09.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There are two problems, which should be paid attention to when using kernel partial least squares (KPLS), one is overfitting and another is how to eliminate the useless information mixed in the independent variables X. In this paper, the stochastic gradient boosting (SGB) method is adopted to solve the overfitting problems and a new method called kernel net analyte preprocessing (KNAP) is proposed to remove undesirable systematic variation in X that is unrelated to Y. Thus, by combining the two methods, a final modeling approach named modified KPLS (MKPLS) is proposed. Two simulation experiments are carried out to evaluate the performance of the MKPLS method. The simulation results show that MKPLS method can not only be resistant to overfitting but also improve the prediction accuracy. (C) 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keyword:
Kernel partial least squares
Stochastic gradient boosting
Kernel net analyte preprocessing
Overfitting
Multivariate data modeling
Data preprocessing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
9.0K
被引数:
2.1W
机构
引用论文
Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram基于心电图谱图模式的阻塞性睡眠呼吸暂停分类
PREAI
分享收藏Improved multi-scale kernel principal component analysis and its application for fault detection
PREAI
分享收藏
Highly efficient electrocatalytic hydrogen production by nickel promoted molybdenum sulfide microspheres catalysts镍促进硫化钼微球催化剂的高效电催化制氢.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏Impact of Revised Time of Use Tariff on Variable Renewable Energy Curtailment on Jeju Island修订后的使用时间关税对济州岛可变可再生能源削减的影响Electronics
OAAI
ElectronicsIF0
OAAI分享收藏
查看更多内容