CAJ | 학술논문

在提升支持向量机分类算法精度的问题上，提出了一种基于混合高斯模型和空间模糊度的支持向量机算法。该算法通过采用多维混合高斯模型的求带分布密度概率函数的二次规划问题的最优解，减少不同的输入样本数据对分类超平面造成的影响，引入了优化后的空间模糊度因子和空间模糊度函数。在实验仿真上，采用了人工选择的样本数据集和 UCI 机器学习数据库中的样本数据集进行多次实验，最后通过对比分析的方法突出了算法在分类精度上的优势。
재제승지지향량궤분류산법정도적문제상，제출료일충기우혼합고사모형화공간모호도적지지향량궤산법。해산법통과채용다유혼합고사모형적구대분포밀도개솔함수적이차규화문제적최우해，감소불동적수입양본수거대분류초평면조성적영향，인입료우화후적공간모호도인자화공간모호도함수。재실험방진상，채용료인공선택적양본수거집화 UCI 궤기학습수거고중적양본수거집진행다차실험，최후통과대비분석적방법돌출료산법재분류정도상적우세。
For the problem that to enhance the accuracy of SVMclassification algorithm,this paper proposed an optimal solu-tion based on Gaussian mixture model and spatial ambiguity SVM algorithm.This algorithm used multi-dimensional Gaussian mixture model with quadratic programming problem to seek the probability density function,reduced the different input sample data to hyperplane and introduced the optimized space fuzzy factor and spatial ambiguity function.In the simulation experi-ments,it used artificial selected sample data sets and UCI machine learning database sample data sets.It finally by the com-parative analysis,it highlights the advantages of the proposed method on the classification accuracy.