Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (Vol. 2). IEEE; 2004. p. 985–90.

Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing. 2006;70(1–3):489–501.

Article  Google Scholar 

Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybernet B (Cybernet). 2011;42(2):513–29.

Hoerl AE, Kennard RW. Ridge regression: Applications to nonorthogonal problems. Technometrics. 1970;12(1):69–82.

Article  MATH  Google Scholar 

Deng W, Zheng Q, Chen L. Regularized extreme learning machine. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE; 2009. p. 389–95

Li G, Niu P. An enhanced extreme learning machine based on ridge regression for regression. Neural Comput Appl. 2013;22:803–10.

Article  Google Scholar 

Huang WB, Sun FC. Building feature space of extreme learning machine with sparse denoising stacked-autoencoder. Neurocomputing. 2016;22(174):60–71.

Google Scholar 

Shao Z, Er MJ. Efficient leave-one-out cross-validation-basedregularized extreme learning machine. Neurocomputing. 2016;19(194):260–70.

Chen YY, Wang ZB. Novel variable selection method based on uninformative variable elimination and ridge extreme learning machine: CO gas concentration retrieval trial. Guang pu xue yu guang pu fen xi= Guang pu. 2017;37(1):299–305.

Yu Q, Miche Y, Eirola E, Van Heeswijk M, Séverin E, Lendasse A. Regularized extreme learning machine for regression with missing data. Neurocomputing. 2013;15(102):45–51.

Article  Google Scholar 

Wang H, Li G. Extreme learning machine Cox model for high-dimensional survival analysis. Stat Med. 2019;38(12):2139–56.

Article  MathSciNet  Google Scholar 

Yildirim H, Özkale MR. The performance of ELM based ridge regression via the regularization parameters. Expert Syst Appl. 2019;15(134):225–33.

Article  Google Scholar 

Luo X, Chang X, Ban X. Regression and classification using extreme learning machine based on L1-norm and L2-norm. Neurocomputing. 2016;22(174):179–86.

Article  Google Scholar 

Kejian L. A new class of blased estimate in linear regression. Commun Stat Theor Methods. 1993;22(2):393–402.

Article  MATH  Google Scholar 

Yıldırım H, Özkale MR. An enhanced extreme learning machine based on Liu regression. Neural Process Lett. 2020;52:421–42.

Article  Google Scholar 

Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol. 1996;58(1):267–88.

MathSciNet  MATH  Google Scholar 

Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. OP-ELM: Optimally pruned extreme learning machine. IEEE Trans Neural Netw. 2009;21(1):158–62.

Article  Google Scholar 

Miche Y, Van Heeswijk M, Bas P, Simula O, Lendasse A. TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing. 2011;74(16):2413–21.

Article  Google Scholar 

Martínez-Martínez JM, Escandell-Montero P, Soria-Olivas E, Martín-Guerrero JD, Magdalena-Benedito R, Gómez-Sanchis J. Regularized extreme learning machine for regression problems. Neurocomputing. 2011;74(17):3716–21.

Article  Google Scholar 

Shan P, Zhao Y, Sha X, Wang Q, Lv X, Peng S, Ying Y. Interval lasso regression based extreme learning machine for nonlinear multivariate calibration of near infrared spectroscopic datasets. Anal Methods. 2018;10(25):3011–22.

Article  Google Scholar 

Li R, Wang X, Lei L, Song Y. \( L_ 21 \)-norm based loss function and regularization extreme learning machine. IEEE Access. 2018;18(7):6575–86.

Google Scholar 

Preeti, Bala R, Dagar A, Singh RP. A novel online sequential extreme learning machine with L 2, 1-norm regularization for prediction problems. Appl Intell. 2021;51:1669–89.

Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(2):301–20.

Article  MathSciNet  MATH  Google Scholar 

Yıldırım H, Özkale MR. LL-ELM: a regularized extreme learning machine based on L 1-norm and Liu estimator. Neural Comput Appl. 2021;33(16):10469–84.

Article  Google Scholar 

Rao CR, Mitra SK. Generalized inverse of a matrix and itsapplications. In: Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Theory of Statistics (Vol. 6). University of California Press; 1972. p. 601–21.

Schott JR. Matrix analysis for statistics. John Wiley & Sons; 2016.

Tutz G, Binder H. Boosting ridge regression. Comput Stat Data Anal. 2007;51(12):6044–59.

Article  MathSciNet  MATH  Google Scholar 

Yıldırım H, Özkale MR. A combination of ridge and Liu regressions for extreme learning machine. Soft Comput. 2023;27(5):2493–508.

Sjöstrand K, Clemmensen LH, Larsen R, Einarsson G, Ersbøll B. Spasm: a Matlab toolbox for sparse statistical modeling. J Stat Softw. 2018;23(84):1–37.

Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann Stat. 2004;32(2):407–99.

Article  MathSciNet  MATH  Google Scholar 

Rosset S, Zhu J. Piecewise linear regularized solution paths. Ann Stat. 2007;1:1012–30.

MathSciNet  MATH  Google Scholar 

Zhou DX. On grouping effect of elastic net. Stat Probab Lett. 2013;83(9):2108–12.

Article  MathSciNet  MATH  Google Scholar