Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification
Authors:
- Liang-Rui RenLRLiang-Rui Ren
- Jin-Xing LiuJLJin-Xing Liu
- Ying-Lian GaoYGYing-Lian Gao
- Xiang-Zhen KongXKXiang-Zhen Kong
- Chun-Hou ZhengCZChun-Hou Zheng
- 1 September 2021
- journal article
- research article
- Published by Elsevier in Knowledge-Based Systems
- Vol. 227
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61702299, 61872220, 61873001)
This publication has 47 references indexed in Scilit:
- Outlier-robust extreme learning machine for regression problemsNeurocomputing, 2015
- An Insight into Extreme Learning Machines: Random Neurons, Random Features and KernelsCognitive Computation, 2014
- Multi-view hypergraph learning by patch alignment frameworkNeurocomputing, 2013
- Image classification based on effective extreme learning machineNeurocomputing, 2013
- Robust extreme learning machineNeurocomputing, 2013
- Online sequential extreme learning machine with forgetting mechanismNeurocomputing, 2012
- Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industryExpert Systems with Applications, 2011
- Optimization method based extreme learning machine for classificationNeurocomputing, 2010
- Multilevel spectral hypergraph partitioning with arbitrary vertex sizesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1999
- Spectra, Euclidean representations and clusterings of hypergraphsDiscrete Mathematics, 1993