CAJ | 학술논문

针对传统支持向量数据描述(support vector data description,SVDD)训练中存在的训练速度慢、存储核矩阵需要的空间开销大、计算量大、算法效率低等问题,提出一种基于改进序贯最小优化(SMO)算法的SVDD快速训练方法。该算法针对原有SMO算法仅能处理单类样本的缺陷,提出一种可以处理负样本的改进方法,给出详细的计算推导过程,并针对KKT判定条件、工作集选择等关键问题进行改进。试验证明：与传统的SVDD训练算法相比,基于改进SMO算法的SVDD快速训练方法训练时间短,计算量小,分类准确度高,空间开销小,更适合于大规模数据的快速训练,具有较高的工程应用价值。
침대전통지지향량수거묘술(support vector data description,SVDD)훈련중존재적훈련속도만、존저핵구진수요적공간개소대、계산량대、산법효솔저등문제,제출일충기우개진서관최소우화(SMO)산법적SVDD쾌속훈련방법。해산법침대원유SMO산법부능처리단류양본적결함,제출일충가이처리부양본적개진방법,급출상세적계산추도과정,병침대KKT판정조건、공작집선택등관건문제진행개진。시험증명：여전통적SVDD훈련산법상비,기우개진SMO산법적SVDD쾌속훈련방법훈련시간단,계산량소,분류준학도고,공간개소소,경괄합우대규모수거적쾌속훈련,구유교고적공정응용개치。
A fast training algorithm is presented based on the improved SMO to solve the problems such as low training speed, high cost in large storage space needed for kernel matrix, large amounts of calculation and low efficiency in traditional training algorithms. As the original SMO can only deal with the samples of the same class,an improved SMO which can deal with negative samples is presented. Its calculation and derivation processes have been presented in details and the important problems like KKT judge condition and working set selection have been improved. Experiments show that the improved SMO used in SVDD fast training takes less time, needs smaller calculation and smaller space, and has higher classification accuracy. It is more suitable for large-scale data fast training and has high value in engineering application.