A Simpli.cation on SMO Algorithm and Its Application in Solving ε-SVR with Non-positive Kernels
Sequential Minimal Optimization (SMO) algorithm is very effective when solving large-scale support vector machine (SVM). The existing algorithms need to judge which quadrant the 4 Lagrange multipliers lie in, complicating its implementation. In addition, the existing algorithms all assume that the kernel functions are positive definite or positive semide finite, limiting their applications. Having considered these deficiencies of the traditional ones, a simplified SMO algorithm based on SVR is proposed, and further applied in solving ..- SVR with non-positive Kernels. Compared with the existing algorithms, the simplified one is much easier to be implemented without sacrificing space and time efficiency, and can achieve an ideal regression accuracy under the premise of ensuring convergence. Therefore, it has a certain theoretical and practical significance.
Non-Positive Kernel SMO Algorithm SVR
XiaoJian Zhou YiZhong Ma ZiQiang Cheng LiPing Liu JianJun Wang
Department of Management Science and TechnologyNanjing University of Science and TechnologyNanjing,C Department of Management Science and Technology Nanjing University of Science and Technology Nanjing
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-6
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）