Support vector machines-based quantitative structure - Property relationship for the prediction of heat capacity

被引:33
|
作者
Xue, CX
Zhang, RS
Liu, HX
Liu, MC
Hu, ZD [1 ]
Fan, BT
机构
[1] Lanzhou Univ, Dept Chem, Lanzhou 730000, Peoples R China
[2] Lanzhou Univ, Dept Comp Sci, Lanzhou 730000, Peoples R China
[3] Univ Paris 07, ITODYS, F-75005 Paris, France
关键词
D O I
10.1021/ci049934n
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.
引用
收藏
页码:1267 / 1274
页数:8
相关论文
共 50 条
  • [1] Quantitative structure property relationship models for the prediction of liquid heat capacity
    Yao, XJ
    Fan, BT
    Doucet, JP
    Panaye, A
    Liu, MC
    Zhang, RS
    Zhang, XY
    Hu, ZD
    [J]. QSAR & COMBINATORIAL SCIENCE, 2003, 22 (01): : 29 - 48
  • [2] Support vector machines-based generalized predictive control
    Iplikci, S.
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2006, 16 (17) : 843 - 862
  • [3] Quantitative Structure-Property Relationship Prediction of Gas Heat Capacity for Organic Compounds
    Khajeh, Aboozar
    Modarress, Hamid
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (41) : 13490 - 13495
  • [4] A Support Vector Machines-based rejection technique for speech recognition
    Ma, CX
    Randolph, MA
    Drish, J
    [J]. 2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS: VOL I: SPEECH PROCESSING 1; VOL II: SPEECH PROCESSING 2 IND TECHNOL TRACK DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS NEURALNETWORKS FOR SIGNAL PROCESSING; VOL III: IMAGE & MULTIDIMENSIONAL SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING - VOL IV: SIGNAL PROCESSING FOR COMMUNICATIONS; VOL V: SIGNAL PROCESSING EDUCATION SENSOR ARRAY & MULTICHANNEL SIGNAL PROCESSING AUDIO & ELECTROACOUSTICS; VOL VI: SIGNAL PROCESSING THEORY & METHODS STUDENT FORUM, 2001, : 381 - 384
  • [5] Support vector machines-based modelling of seismic liquefaction potential
    Pal, Mahesh
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, 2006, 30 (10) : 983 - 996
  • [6] Hybrid Support Vector Machines-Based Multi-fault Classification
    Department of Precision Instrument and Mechanology, Tsinghua University, Beijing, 100084, China
    不详
    [J]. J. China Univ. Min. Technol., 2007, 2 (246-250):
  • [7] Quantitative phenotype prediction by support vector machines
    Beerenwinkel, N
    Schmidt, B
    Walter, H
    Kaiser, R
    Lengauer, T
    Hoffmann, D
    Korn, K
    Selbig, J
    [J]. ANTIVIRAL THERAPY, 2002, 7 : S97 - S97
  • [9] Quantitative Structure-Property Relationship Prediction of Liquid Heat Capacity at 298.15 K for Organic Compounds
    Khajeh, Aboozar
    Modarress, Hamid
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (17) : 6251 - 6255
  • [10] Quantitative Structure-Property Relationship studies for predicting flash points of organic compounds using support vector machines
    Pan, Yong
    Jiang, Juncheng
    Wang, Rui
    Cao, Hongyin
    Zhao, Jinbo
    [J]. QSAR & COMBINATORIAL SCIENCE, 2008, 27 (08): : 1013 - 1019