Pattern recognition in chemometrics

被引：100
|
作者：
Brereton, Richard G. [1 ]
机构：
[1] Univ Bristol, Sch Chem, Bristol BS8 1TS, Avon, England
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2015年 / 149卷
关键词：
Pattern recognition; Partial least squares discriminant analysis; SIMCA; Linear discriminant analysis; Historic review; Support vector machines; SUPPORT VECTOR MACHINES; DISCRIMINANT-ANALYSIS; COMPONENTS; UNEQ;
D O I：
10.1016/j.chemolab.2015.06.012
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The origins of chemometrics within chemical pattern recognition of the 1960s and 1970s are described. Trends subsequent to that era have reduced the input of pattern recognition within mainstream chemometrics, with a few approaches such as PLS-DA and SIMCA becoming dominant. Meanwhile vibrant and ever expanding literature has developed within machine learning and applied statistics which has hardly touched the chemometric community. Within the wider scientific community, chemometric originated pattern recognition techniques such as PLS-DA have been widely adopted largely due to the existence of widespread packages, but are widely misunderstood and sometimes misapplied. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：90 / 96
页数：7
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