Matches in SemOpenAlex for { <https://semopenalex.org/work/W2079761966> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W2079761966 endingPage "255" @default.
- W2079761966 startingPage "243" @default.
- W2079761966 abstract "Part 1 explained multiplicative scatter correction (MSC), the building of a principal component regression (PCR) model and how the test data can be used in prediction. Emphasis was on data pretreatment for linearistion and on spectral/chemical interpretation of the results. Part 2 discusses partial least squares (PLS or PLSR) regression. The data set prepared in Part 1 is also used here. Details on data pretreatment are, therefore, not repeated. Some details of PLS modeling are explained using the calculations of the example. Also, the interpretation of the PLS model gets some attention. Neural network calculation results are included for comparison. Artifical neural networks (ANN) are non-linear, so linearisation is not considered necessary. Latent variable regression methods such as PLS and PCR and ANNs are all successive approximations to the unknown function y = f(x) that forms the basis of all calibration methods. In latent variable regression, the rank of the model determines the degree of approximation. In ANNs, the number of hidden nodes and the number of iterations determine the degree of approximation." @default.
- W2079761966 created "2016-06-24" @default.
- W2079761966 creator A5028442967 @default.
- W2079761966 creator A5045764453 @default.
- W2079761966 creator A5066323251 @default.
- W2079761966 creator A5082707858 @default.
- W2079761966 date "1996-01-01" @default.
- W2079761966 modified "2023-10-16" @default.
- W2079761966 title "A Calibration Tutorial for Spectral Data. Part 2. Partial Least Squares Regression Using Matlab and Some Neural Network Results" @default.
- W2079761966 cites W1545146804 @default.
- W2079761966 cites W2008159043 @default.
- W2079761966 cites W2015596806 @default.
- W2079761966 cites W2027197837 @default.
- W2079761966 cites W2034193997 @default.
- W2079761966 cites W2087883561 @default.
- W2079761966 cites W2103496339 @default.
- W2079761966 cites W2137983211 @default.
- W2079761966 cites W2143956139 @default.
- W2079761966 cites W2796847809 @default.
- W2079761966 cites W4300402905 @default.
- W2079761966 doi "https://doi.org/10.1255/jnirs.94" @default.
- W2079761966 hasPublicationYear "1996" @default.
- W2079761966 type Work @default.
- W2079761966 sameAs 2079761966 @default.
- W2079761966 citedByCount "12" @default.
- W2079761966 countsByYear W20797619662019 @default.
- W2079761966 countsByYear W20797619662020 @default.
- W2079761966 countsByYear W20797619662021 @default.
- W2079761966 countsByYear W20797619662022 @default.
- W2079761966 crossrefType "journal-article" @default.
- W2079761966 hasAuthorship W2079761966A5028442967 @default.
- W2079761966 hasAuthorship W2079761966A5045764453 @default.
- W2079761966 hasAuthorship W2079761966A5066323251 @default.
- W2079761966 hasAuthorship W2079761966A5082707858 @default.
- W2079761966 hasConcept C105795698 @default.
- W2079761966 hasConcept C152877465 @default.
- W2079761966 hasConcept C153180895 @default.
- W2079761966 hasConcept C154945302 @default.
- W2079761966 hasConcept C165838908 @default.
- W2079761966 hasConcept C177264268 @default.
- W2079761966 hasConcept C185429906 @default.
- W2079761966 hasConcept C199360897 @default.
- W2079761966 hasConcept C22354355 @default.
- W2079761966 hasConcept C27438332 @default.
- W2079761966 hasConcept C33923547 @default.
- W2079761966 hasConcept C41008148 @default.
- W2079761966 hasConcept C48921125 @default.
- W2079761966 hasConcept C50644808 @default.
- W2079761966 hasConcept C51167844 @default.
- W2079761966 hasConcept C58489278 @default.
- W2079761966 hasConcept C74887250 @default.
- W2079761966 hasConcept C83546350 @default.
- W2079761966 hasConcept C9936470 @default.
- W2079761966 hasConceptScore W2079761966C105795698 @default.
- W2079761966 hasConceptScore W2079761966C152877465 @default.
- W2079761966 hasConceptScore W2079761966C153180895 @default.
- W2079761966 hasConceptScore W2079761966C154945302 @default.
- W2079761966 hasConceptScore W2079761966C165838908 @default.
- W2079761966 hasConceptScore W2079761966C177264268 @default.
- W2079761966 hasConceptScore W2079761966C185429906 @default.
- W2079761966 hasConceptScore W2079761966C199360897 @default.
- W2079761966 hasConceptScore W2079761966C22354355 @default.
- W2079761966 hasConceptScore W2079761966C27438332 @default.
- W2079761966 hasConceptScore W2079761966C33923547 @default.
- W2079761966 hasConceptScore W2079761966C41008148 @default.
- W2079761966 hasConceptScore W2079761966C48921125 @default.
- W2079761966 hasConceptScore W2079761966C50644808 @default.
- W2079761966 hasConceptScore W2079761966C51167844 @default.
- W2079761966 hasConceptScore W2079761966C58489278 @default.
- W2079761966 hasConceptScore W2079761966C74887250 @default.
- W2079761966 hasConceptScore W2079761966C83546350 @default.
- W2079761966 hasConceptScore W2079761966C9936470 @default.
- W2079761966 hasIssue "1" @default.
- W2079761966 hasLocation W20797619661 @default.
- W2079761966 hasOpenAccess W2079761966 @default.
- W2079761966 hasPrimaryLocation W20797619661 @default.
- W2079761966 hasRelatedWork W2021552986 @default.
- W2079761966 hasRelatedWork W2029697302 @default.
- W2079761966 hasRelatedWork W2057527240 @default.
- W2079761966 hasRelatedWork W2094481184 @default.
- W2079761966 hasRelatedWork W2114737495 @default.
- W2079761966 hasRelatedWork W2327431991 @default.
- W2079761966 hasRelatedWork W2592325956 @default.
- W2079761966 hasRelatedWork W2977185326 @default.
- W2079761966 hasRelatedWork W4210724690 @default.
- W2079761966 hasRelatedWork W4225151263 @default.
- W2079761966 hasVolume "4" @default.
- W2079761966 isParatext "false" @default.
- W2079761966 isRetracted "false" @default.
- W2079761966 magId "2079761966" @default.
- W2079761966 workType "article" @default.