【摘要】 本文提出一种基于独立成分分析(ICA)与支持向量机(SVM)的孤立点挖掘模型——ISOM模型,用ICA时观测到的多维随机向量进行独立成分分解,用SVM估计独立成分的密度函数,克服了传统孤立点挖掘方法的一些缺点,为数据挖掘提供了一种有效的方法,并通过实验验证了该模型的合理性与正确性。更多还原

【Abstract】 ISOM,Outlier Mining Model Based on ICA & SVM,is presented in this paper.This model transforms an observed multidimensional random vector into mutually independent components by ICA and estimates independent components’ density function by SVM.Overcoming the defects of traditional outlier mining,the model of ISOM pro- vides an efficient method for data mining,and its correctness and reasonableness ars also validated by the experiment results in this paper.更多还原