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Extreme learning machine( ELM) had a high learning speed and a good generalization ablity.Smoothing strategy was an important technology for non-smooth problems. By combining a smoothing technique with ELM,a smooth ELM( SELM) framework was proposed. Moreover,the Newton-Armijo algorithm was used to solve the SELM,and resulting algorithm converged globally and quadratically. The proposed SELM had less decision variables and better abitities to deal with nonlinear problems than the existing smooth support vector machine. Numerical experiments demonstrated that the speed of SELM was much faster than that of the existing ELM algorithms based on optimization theory. Compared with other popular support vector machines,the proposed SELM achieved better or similar generalization. The results demonstrated the feasibility and effectiveness of the proposed algorithm.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2016097
China Classification Code:TP18
Citation Information:
[1]YANG Liming,ZHANG Siyun,REN Zhuo ,et al.A Smooth Extreme Learning Machine for Classification[J].Journal of Zhengzhou University(Natural Science Edition),2016,48(03):51-56.DOI:10.13705/j.issn.1671-6841.2016097.
Fund Information:
国家自然科学基金资助项目(11471010)
2016-10-17
2016-10-17
2016-10-17