摘要
支持向量机(SVM)算法是特别适合于用有限已知样本训练建模,进而预报未知样本属性的模式识别新算法。本工作中应用支持向量回归算法和多环芳烃分子的环数、分子宽度、长度、体积、顶联接指数和边联接指数等几何参数作数据挖掘,总结了多环芳烃在空气-正辛醇分配比、多环芳烃在土壤中吸附参数、多环芳烃的生物浓度因子与分子几何参数关系的数学模型。用留一法证明:数学模型的预报可靠性较PLS算法建立的数学模型略优。
Support vector machine proposed by Vapnik is a newly developed technique for data mining. It is suitable for the data processing based on finite number of training samples, with special techm'que to restrict overfitting. In this work, support vector regression has been used for correlating and modeling the relationships between the geometrical parameters and environmental behaviors of polycyclic aromatic hydrocar-bons. The prediction ability of the mathematical models obtained is somewhat better than that obtained by PLS regression.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2002年第6期749-751,共3页
Computers and Applied Chemistry
基金
国家自然科学基金委和美国福特公司联合资助(9716214)
