CAJ | 학술논문

改进了基于参数化间隔的双子支持向量机算法的预处理过程，在数据预处理阶段使用了主成分分析法对数据进行降维，提出了基于主成分分析的参数化间隔双子支持向量机，从而加快了整个算法的训练速度．公共数据库上的实验结果显示了该算法的优秀分类能力，对高维数据集的降维效果也比较成功．最后，将这种算法应用到手写体数字识别技术上，实验结果显示出该算法较好的分类性能．
개진료기우삼수화간격적쌍자지지향량궤산법적예처리과정，재수거예처리계단사용료주성분분석법대수거진행강유，제출료기우주성분분석적삼수화간격쌍자지지향량궤，종이가쾌료정개산법적훈련속도．공공수거고상적실험결과현시료해산법적우수분류능력，대고유수거집적강유효과야비교성공．최후，장저충산법응용도수사체수자식별기술상，실험결과현시출해산법교호적분류성능．
The pretreatment process of twin support vector machine based on Parametric-margin has been improved, principal component analysis to reduce the dimensionality of the data is used in the data preprocessing stage. The twin support vector machines based on principal component analysis speeds up the training process. Experiments on public available datasets show excellent classification ability. Dimensionality reduction of the high-dimensional dataset is also more successful. Finally, this algorithm is applied on handwritten numeral recognition. The experimental results show the effective of our algorithm.