CAJ | 학술논문

针对脱机手写体汉字特征复杂和类别多样的特点,基于 SVM 数学模型,采用了一种不确定性二叉树与SVM 相结合的分类识别方法设计了一种多类分类器,该设计方法在保证识别准确率的情况下大大减少了支持向量机的数量,简化了二叉树模型,能快速辨识并删除多余的枝节,并具有一定的容错率,加快了辨识速度.实验结果表明,采用不确定性二叉树 SVM 设计的多类分类器有效地降低了拒识率和漏识率,保证了识别的准确率,提高了识别速度.
침대탈궤수사체한자특정복잡화유별다양적특점,기우 SVM 수학모형,채용료일충불학정성이차수여SVM 상결합적분류식별방법설계료일충다류분류기,해설계방법재보증식별준학솔적정황하대대감소료지지향량궤적수량,간화료이차수모형,능쾌속변식병산제다여적지절,병구유일정적용착솔,가쾌료변식속도.실험결과표명,채용불학정성이차수 SVM 설계적다류분류기유효지강저료거식솔화루식솔,보증료식별적준학솔,제고료식별속도.
By adopting the classification and recognition method, a multi -class classifier based on SVM model is established due to the characteristics of handwritten Chinese character, such as the complexity of characteristics and diversity of category.The design method with fault -tolerant rate can reduce number of support vector machine, improve the recognition speed, simplify bi-nary tree model, quickly identify and remove the extra minor under the condition of the accuracy of the identification .The experi-mental results show that the multi -class classifier based on uncertainty binary tree SVM can reduce the rejection rate and leakage identification rate effectively to ensure the recognition accuracy and improve the recognition speed .