An Incremental Support Vector Machine Learning Based on Minimum Shifting of Classification Hyperplane

摘要：

standard support vector machine has not incremental learning ability. A new incremental support vector machine learning is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vector machine. The goal concept was updated by incremental learning. Each training procedure only includes new training data. The time complexity is independent of whole training set Compared with the other version, the training speed of this approach is improved and the increment of hyperplane is reduced.

关键词： support vector machine incremental algorithm shifting

作者: Fengqing Han Cheng Wen Wenjuan Zhao

作者单位: The Computer Science and Engineering School Chongqing University of Technology Chongqing, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 484-488

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）