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Support vector regression that takes into consideration the importance of explanatory variables
Author(s) -
Kaneko Hiromasa
Publication year - 2021
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3327
Subject(s) - support vector machine , quantitative structure–activity relationship , mathematics , regression analysis , regression , gaussian function , kernel (algebra) , euclidean distance , linear regression , random forest , artificial intelligence , computer science , gaussian , data mining , statistics , machine learning , chemistry , discrete mathematics , computational chemistry
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