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Kernel PLS based prediction model construction and simulation on theoretical cases - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper discusses the use of kernel PLS, a multivariate statistical method, for prediction.
2. Kernel PLS is used to establish relationships between input and output variables in a high-dimensional space, making it effective for nonlinear conditions.
3. Kernel PLS has been applied in many areas such as chemometrics, bioinformatics and neuroscience.
Article analysis:
The article provides an overview of the use of kernel partial least squares (KPLS) for prediction purposes. The article is well written and provides a comprehensive overview of the topic, including its applications in various fields such as chemometrics, bioinformatics and neuroscience. The article also provides examples to demonstrate the effectiveness of KPLS in nonlinear cases.
The article does not provide any evidence or data to support its claims about the effectiveness of KPLS in nonlinear cases or its applications in various fields. Furthermore, there is no discussion on potential risks associated with using KPLS or any counterarguments that could be made against it. Additionally, there is no mention of other methods that could be used for prediction purposes and how they compare to KPLS. This lack of comparison makes it difficult to assess the trustworthiness and reliability of the article's claims about KPLS being an effective method for prediction purposes.
In conclusion, while this article provides a comprehensive overview of KPLS and its applications, it lacks evidence to support its claims about its effectiveness and fails to explore potential risks associated with using this method or alternative methods that could be used instead.