SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS
Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-SVR) were, respectively, used to construct quantitative structure-property relations...
Guardado en:
| Autores principales: | , , |
|---|---|
| Lenguaje: | English |
| Publicado: |
Sociedad Chilena de Química
2011
|
| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072011000300006 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:scielo:S0717-97072011000300006 |
|---|---|
| record_format |
dspace |
| spelling |
oai:scielo:S0717-970720110003000062012-01-11SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERSYU,XINLIANGWANG,XUEYECHEN,JIANFANG free-radical copolymerizations Q-e scheme quantum chemical descriptors structure-property relations support vector machine Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-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 CH3CH2C¹H2-C²HR³· (C¹H2=C²HR³ + CH3CH2· - CH3CH2C¹H2-C²HR³·). The optimum å-SVR model of lnQ (C= 9, å =0.05 and ã =0.2) and the optimum v-SVR model of e (C=100, v = 0.5 and ã =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.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.56 n.3 20112011-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072011000300006en10.4067/S0717-97072011000300006 |
| institution |
Scielo Chile |
| collection |
Scielo Chile |
| language |
English |
| topic |
free-radical copolymerizations Q-e scheme quantum chemical descriptors structure-property relations support vector machine |
| spellingShingle |
free-radical copolymerizations Q-e scheme quantum chemical descriptors structure-property relations support vector machine YU,XINLIANG WANG,XUEYE CHEN,JIANFANG SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| description |
Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-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 CH3CH2C¹H2-C²HR³· (C¹H2=C²HR³ + CH3CH2· - CH3CH2C¹H2-C²HR³·). The optimum å-SVR model of lnQ (C= 9, å =0.05 and ã =0.2) and the optimum v-SVR model of e (C=100, v = 0.5 and ã =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. |
| author |
YU,XINLIANG WANG,XUEYE CHEN,JIANFANG |
| author_facet |
YU,XINLIANG WANG,XUEYE CHEN,JIANFANG |
| author_sort |
YU,XINLIANG |
| title |
SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| title_short |
SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| title_full |
SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| title_fullStr |
SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| title_full_unstemmed |
SUPPORT VECTOR MACHINE REGRESSION FOR REACTIVITY PARAMETERS OF VINYL MONOMERS |
| title_sort |
support vector machine regression for reactivity parameters of vinyl monomers |
| publisher |
Sociedad Chilena de Química |
| publishDate |
2011 |
| url |
http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072011000300006 |
| work_keys_str_mv |
AT yuxinliang supportvectormachineregressionforreactivityparametersofvinylmonomers AT wangxueye supportvectormachineregressionforreactivityparametersofvinylmonomers AT chenjianfang supportvectormachineregressionforreactivityparametersofvinylmonomers |
| _version_ |
1718445449864544256 |