[ 1 ] Bao YY, Zhu YM, Du WL, Zhong WM, Qian F. A distributed PCA-TSS based soft sensor for raw meal fineness in VRM system. Control Eng Pract 2019;90:38‒49. 链接1

[ 2 ] Zhong WM, Jiang C, Peng X, Li Z, Qian F. Online quality prediction of industrial terephthalic acid hydropurification process using modified regularized slow-feature analysis. Ind Eng Chem Res 2018;57(29):9604‒14. 链接1

[ 3 ] Lu B, Chiang L. Semi-supervised online soft sensor maintenance experiences in the chemical industry. J Process Contr 2018;67:23‒34. 链接1

[ 4 ] Li YG, Gui WH, Yang CH, Xie YF. Soft sensor and expert control for blending and digestion process in alumina metallurgical industry. J Process Contr 2013;23(7):1012‒21. 链接1

[ 5 ] Song YC, Zhou H, Wang PF, Yang MJ. Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks. J Chem Thermodyn 2019;135:86‒96. 链接1

[ 6 ] Touzani S, Granderson J, Fernandes S. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energ Build 2018;158:1533‒43. 链接1

[ 7 ] Fernández-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M. An extensive experimental survey of regression methods. Neural Netw 2019;111:11‒34. 链接1

[ 8 ] Yu J. Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes. Ind Eng Chem Res 2012;51(40):13227‒37. 链接1

[ 9 ] Yuan XF, Ge ZQ, Song ZH. Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes. Ind Eng Chem Res 2014;53(35):13736‒49. 链接1

[10] Gou JP, Ma HX, Ou WH, Zeng SN, Rao YB, Yang HB. A generalized mean distance-based k-nearest neighbor classifier. Expert Syst Appl 2019;115:356‒72. 链接1

[11] Juez-Gil M, Erdakov IN, Bustillo A, Pimenov DY. A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes. J Adv Res 2019;18:173‒84. 链接1

[12] Suarez A, Lutsko JF. Globally optimal fuzzy decision trees for classification and regression. IEEE Trans Pattern Anal Mach Intell 1999;21(12):1297‒311. 链接1

[13] Huang GB, Zhou HM, Ding XJ, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 2012;42(2):513‒29. 链接1

[14] Vilela LFS, Leme RC, Pinheiro CAM, Carpinteiro OAS. Forecasting financial series using clustering methods and support vector regression. Artif Intell Rev 2019;52(2):743‒73.. 链接1

[15] Paul A, Mukherjee DP. Reinforced quasi-random forest. Pattern Recognit 2019;94:13‒24. 链接1

[16] Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 2012;67:93‒104. 链接1

[17] Freund Y. Boosting a weak learning algorithm by majority. Inf Comput 1995;121(2):256‒85. 链接1

[18] Chen TQ, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13‒17; San Francisco, CA, USA; 2016. p. 785‒94. 链接1

[19] Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 2002;35(5‒6):352‒9.

[20] Zhou ZH, Wu JX, Tang W. Ensembling neural networks: many could be better than all. Artif Intell 2002;137(1‒2):239‒63.

[21] Liu WB, Wang ZD, Liu XH, Zeng NY, Liu YR, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing 2017;234:11‒26. 链接1

[22] Martos G, Muñoz A, González J. On the generalization of the Mahalanobis distance. In: Ruiz-Shulcloper J, Sanniti di Baja G, editors. Progress in pattern recognition, image analysis, computer vision, and applications. Berlin: Springer; 2013. p. 125‒32. 链接1

[23] Atkeson CG, Moore AW, Schaal S. Locally weighted learning. Artif Intell Rev 1997;11:11‒73. 链接1

[24] Zhang JJ. Identification of moving loads using a local linear embedding algorithm. J Vib Control 2019;25(11):1780‒90. 链接1

[25] Loia V, Tomasiello S, Vaccaro A, Gao JW. Using local learning with fuzzy transform: application to short term forecasting problems. Fuzzy Optim Decis Making 2020;19(1):13‒32. 链接1

[26] Weinberger KQ, Saul LK. Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 2009;10:207‒44. 链接1

[27] Nguyen B, Morell C, De Baets B. Large-scale distance metric learning for k-nearest neighbors regression. Neurocomputing 2016;214:805‒14. 链接1

[28] Xing EP, Ng AY, Jordan MI, Russell S. Distance metric learning, with application to clustering with side-information. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems 15: proceedings of the 2002 conference. Cambridge: A Bradford Book; 2003. p. 521‒8.

[29] Duan YQ, Lu JW, Zheng WZ, Zhou J. Deep adversarial metric learning. IEEE Trans Image Process 2020;29:2037‒51. 链接1

[30] Song HO, Xiang Y, Jegelka S, Savarese S. Deep metric learning via lifted structured feature embedding. In: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 26‒Jul 1; Las Vegas, NV, USA; 2016. p. 4004‒12. 链接1

[31] Cui Y, Zhou F, Lin YQ, Belongie S. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 26‒Jul 1; Las Vegas, NV, USA; 2016. p. 1153‒62. 链接1

[32] ALzubi JA, Bharathikannan B, Tanwar S, Manikandan R, Khanna A, Thaventhiran C. Boosted neural network ensemble classification for lung cancer disease diagnosis. Appl Soft Comput 2019;80:579‒91. 链接1

[33] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015. arXiv:1502.03167.

[34] Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. 2012. arXiv:1207.0580.

[35] Kay S. Can detectability be improved by adding noise? IEEE Signal Process Lett 2000;7(1):8‒10. 链接1

[36] Boyd V, Vandenberghe L, Faybusovich L. Convex optimization. IEEE Trans Automat Contr 2006;51(11):1859. 链接1

[37] Cortez P, Morais A. A data mining approach to predict forest fires using meteorological data. In: NevesJM, SantosMF, MachadoJM, editors. New trends in artificial intelligence: proceedings of the 13th Portuguese Conference on Artificial Intelligence; 2007 Dec 3‒7; Guimarães, Portugal; 2007. p. 512‒23. French. 链接1

[38] Cortez P, Cerdeira A, Almeida F, Matos T, Reis J. Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 2009;47(4):547‒53. 链接1

[39] De Vito S, Massera E, Piga M, Martinotto L, Di Francia G. On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators B Chem 2008;129(2):750‒7. 链接1

[40] Ke GL, Meng Q, Finley T, Wang TF, Chen W, Ma WD, et al. LightGBM: a highly efficient gradient boosting decision tree. In: GuyonI, Von LuxburgU, BengioS, WallachH, FergusR, VishwanathanS, et al., editors. Proceedings of the 31st Annual Conference on Neural Information Processing Systems; 2017 Dec 4‒9; Long Beach, CA, USA; 2017.

[41] Guo HF, Tang RM, Ye YM, Li ZG, He XQ. DeepFM: a factorization-machine based neural network for CTR prediction. In: SierraC, editor. Proceedings of the 26th International Joint Conference on Artificial Intelligence; 2017 Aug 19‒25; Melbourne, VIC, Australia; 2017. p. 1725‒31. 链接1

[42] Zhang L, Shen WC, Huang JH, Li SJ, Pan G. Field-aware neural factorization machine for click-through rate prediction. IEEE Access 2019;7:75032‒40. 链接1

[43] Huang JY, Zhang X, Fang BX. CoStock: a DeepFM model for stock market prediction with attentional embeddings. In: Proceedings of 2019 IEEE International Conference on Big Data; 2019 Dec 9‒12; AngelesLos, CA, USA. New York City: IEEE; 2019. p. 5522‒31. 链接1