CAJ | 학술논문

多类支持向量机在实际应用领域是一个非常重要的问题。广泛应用的多类SVM方法包括：一对一、一对多和DAG等。众多实验表明一对一方法通常具有较高分类准确率,但传统一对一方法测试时间较长限制了其在大数据量识别任务中的应用。针对一对一支持向量机方法进行了改进,提出了一种改进的支持向量机,并采用其对多目标图像进行了分割研究。实验结果表明,支持向量机方法是一种很有前景的图像分割技术。
다류지지향량궤재실제응용영역시일개비상중요적문제。엄범응용적다류SVM방법포괄：일대일、일대다화DAG등。음다실험표명일대일방법통상구유교고분류준학솔,단전통일대일방법측시시간교장한제료기재대수거량식별임무중적응용。침대일대일지지향량궤방법진행료개진,제출료일충개진적지지향량궤,병채용기대다목표도상진행료분할연구。실험결과표명,지지향량궤방법시일충흔유전경적도상분할기술。
Multi-class support vector machine has a very important issue in the field of practical application.The widely used multi-class SVM methods include： one-against-one,one-against-all and DAG.Many experiments show that one-against-one method usually has high classification accuracy.But the traditional one-against-one method takes a longer time;it limits its application in the amount of data in the recognition task.An improved support vector machine was suggested which is based on one-against-one support vector machine and used to image segmentation.Experiments show that support vector machine approach is a promising image segmentation technique.