184 related articles for article (PubMed ID: 30822738)
1. Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration.
Shan P; Zhao Y; Wang Q; Sha X; Lv X; Peng S; Ying Y
Spectrochim Acta A Mol Biomol Spectrosc; 2019 May; 215():97-111. PubMed ID: 30822738
[TBL] [Abstract][Full Text] [Related]
2. Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy.
Chen H; Tan C; Lin Z
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Mar; 229():117982. PubMed ID: 31935651
[TBL] [Abstract][Full Text] [Related]
3. Dual stacked partial least squares for analysis of near-infrared spectra.
Bi Y; Xie Q; Peng S; Tang L; Hu Y; Tan J; Zhao Y; Li C
Anal Chim Acta; 2013 Aug; 792():19-27. PubMed ID: 23910963
[TBL] [Abstract][Full Text] [Related]
4. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.
Abuassba AOM; Zhang D; Luo X; Shaheryar A; Ali H
Comput Intell Neurosci; 2017; 2017():3405463. PubMed ID: 28546808
[TBL] [Abstract][Full Text] [Related]
5. Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: application of non-bilinear voltammetric data for exploiting first-order advantage.
Gholivand MB; Jalalvand AR; Goicoechea HC; Skov T
Talanta; 2014 Feb; 119():553-63. PubMed ID: 24401455
[TBL] [Abstract][Full Text] [Related]
6. Long-term time series prediction using OP-ELM.
Grigorievskiy A; Miche Y; Ventelä AM; Séverin E; Lendasse A
Neural Netw; 2014 Mar; 51():50-6. PubMed ID: 24365536
[TBL] [Abstract][Full Text] [Related]
7. Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.
Zhao F; Liu Y; Huo K; Zhang S; Zhang Z
Sensors (Basel); 2018 Jan; 18(1):. PubMed ID: 29320453
[TBL] [Abstract][Full Text] [Related]
8. Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem.
Zhang MY; Wu Q; Xu ZZ
Math Biosci Eng; 2019 May; 16(5):4692-4707. PubMed ID: 31499684
[TBL] [Abstract][Full Text] [Related]
9. Parsimonious extreme learning machine using recursive orthogonal least squares.
Wang N; Er MJ; Han M
IEEE Trans Neural Netw Learn Syst; 2014 Oct; 25(10):1828-41. PubMed ID: 25291736
[TBL] [Abstract][Full Text] [Related]
10. A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.
He YL; Geng ZQ; Xu Y; Zhu QX
ISA Trans; 2015 Sep; 58():533-42. PubMed ID: 26112928
[TBL] [Abstract][Full Text] [Related]
11. Extreme Learning Machine With Affine Transformation Inputs in an Activation Function.
Cao J; Zhang K; Yong H; Lai X; Chen B; Lin Z
IEEE Trans Neural Netw Learn Syst; 2019 Jul; 30(7):2093-2107. PubMed ID: 30442621
[TBL] [Abstract][Full Text] [Related]
12. [An Improved ELM Algorithm for Near Infrared Spectral Quantitative Analysis].
Zhang HG; Lu JG
Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Sep; 36(9):2784-8. PubMed ID: 30084595
[TBL] [Abstract][Full Text] [Related]
13. Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.
Riccardi A; Fernández-Navarro F; Carloni S
IEEE Trans Cybern; 2014 Oct; 44(10):1898-909. PubMed ID: 25222730
[TBL] [Abstract][Full Text] [Related]
14. Effects of nonlinearities and uncorrelated or correlated errors in realistic simulated data on the prediction abilities of augmented classical least squares and partial least squares.
Melgaard DK; Haaland DM
Appl Spectrosc; 2004 Sep; 58(9):1065-73. PubMed ID: 15479523
[TBL] [Abstract][Full Text] [Related]
15. Is extreme learning machine feasible? A theoretical assessment (part II).
Lin S; Liu X; Fang J; Xu Z
IEEE Trans Neural Netw Learn Syst; 2015 Jan; 26(1):21-34. PubMed ID: 25069128
[TBL] [Abstract][Full Text] [Related]
16. Extreme learning machine for regression and multiclass classification.
Huang GB; Zhou H; Ding X; Zhang R
IEEE Trans Syst Man Cybern B Cybern; 2012 Apr; 42(2):513-29. PubMed ID: 21984515
[TBL] [Abstract][Full Text] [Related]
17. [Near infrared spectroscopy quantitative analysis model based on incremental neural network with partial least squares].
Cao H; Li DH; Liu L; Zhou Y
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Oct; 34(10):2799-803. PubMed ID: 25739228
[TBL] [Abstract][Full Text] [Related]
18. SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine.
Guo H; Wang J; Ao W; He Y
Comput Intell Neurosci; 2018; 2018():4058403. PubMed ID: 30046300
[TBL] [Abstract][Full Text] [Related]
19. Online Sequential Extreme Learning Machine With Kernels.
Scardapane S; Comminiello D; Scarpiniti M; Uncini A
IEEE Trans Neural Netw Learn Syst; 2015 Sep; 26(9):2214-20. PubMed ID: 25561597
[TBL] [Abstract][Full Text] [Related]
20. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.
Jian Y; Huang D; Yan J; Lu K; Huang Y; Wen T; Zeng T; Zhong S; Xie Q
Sensors (Basel); 2017 Jun; 17(6):. PubMed ID: 28629202
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
