所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：Neural Comput. Appl.
摘要：Computational complexity and sample selection are two main factors that limited the performance of online sequential extreme learning machines (OS-ELMs). This paper proposes a new model that introduces the concept of hybrid kernel and sample selection method based on an online learning model using a membership function. In other words, an online sequential extreme learning machine based on a hybrid kernel function (HKOS-ELM) is presented. The algorithm only calculates the kernel function to determine the final output function, mostly solving the computational complexity of the algorithm. The hybrid kernel function proposed in this paper has the advantages of strong learning ability and good generalization performance of single kernel function. Based on the classification essence of the OS-ELM classification, the membership function is introduced into the sample selection to remove the noise point and the outlier point. The experimental results showed that the HKOS-ELM algorithm adding the membership degree with mixed kernel functions preserves the advantages of kernel functions and online learning and improves the classification performance of the system. © 2018, The Natural Computing Applications Forum.
ISSN号：0941-0643
是否译文：否
发表时间：2019-09-01
合写作者：Zhang, Senyue,Wang, Qingjun,Wang, Nan
通讯作者：Zhang, Senyue,谭文安
发表时间：2019-09-01