In this paper we have pointed out an important source of inefficiency in SMO algorithm that is caused bythe operation with a single threshod value. We have suggested modifications of SMO algorithm that overcome thepro...In this paper we have pointed out an important source of inefficiency in SMO algorithm that is caused bythe operation with a single threshod value. We have suggested modifications of SMO algorithm that overcome theproblem by efficiently maintaining and updating two threshold parameters. Our experiments show that these modifi-cations speed up the SMO algorithm.展开更多
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...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。展开更多
文摘In this paper we have pointed out an important source of inefficiency in SMO algorithm that is caused bythe operation with a single threshod value. We have suggested modifications of SMO algorithm that overcome theproblem by efficiently maintaining and updating two threshold parameters. Our experiments show that these modifi-cations speed up the SMO algorithm.
文摘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。
