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- W2164194837 abstract "ABSTRACT Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, e- support vector regression (e-SVR) and ν-support vector regression (ν-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q - e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to developed the SVR models were calculated from styrene and radicals with structures CH 3 CH 2 C 1 H 2 –C 2 HR 3 • (C 1 H 2 =C 2 HR 3 + CH 3 CH 2 • → CH 3 CH 2 C 1 H 2 –C 2 HR 3 •). The optimum e-SVR model of ln Q ( C = 9, e =0.05 and γ =0.2) and the optimum ν-SVR model of e ( C =100, ν = 0.5 and γ =0.4) produced low root mean square (rms) errors for prediction sets: 0.318 and 0.266, respectively. Thus, applying SVR to predict parameters Q and e is successful. Keywords: free-radical copolymerizations;" @default.
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- W2164194837 date "2011-01-01" @default.
- W2164194837 modified "2023-10-16" @default.
- W2164194837 title "SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS" @default.
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- W2164194837 doi "https://doi.org/10.4067/s0717-97072011000300006" @default.
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