Label Rectification Learning through Kernel Extreme Learning Machine
نویسندگان
چکیده
منابع مشابه
Multiple kernel extreme learning machine
Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of kernels, which is indeed crucial to the perfo...
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Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; [email protected] (J.X.); [email protected] (C.J.); [email protected] (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; [email protected] 3 Department o...
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The extreme learning machine (ELM) is a newly emerging supervised learning method. In order to use the information provided by unlabeled samples and improve the performance of the ELM, we deformed the kernel in the ELM by modeling the marginal distribution with the graph Laplacian, which is built with both labeled and unlabeled samples. We further approximated the deformed kernel by means of ra...
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In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid functions. In addition, we show that a low-rank decomposition...
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ژورنال
عنوان ژورنال: Wireless Communications and Mobile Computing
سال: 2021
ISSN: 1530-8677,1530-8669
DOI: 10.1155/2021/6669081