[1] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20 (3): 273-297.
[2] YANG L, YANG S, ZHANG R, et al. Sparse least square support vector machine via coupled compressive pruning[J]. Neurocomputing, 2014, 131: 77-86.
[3] DING S, HUA X. Recursive least squares projection twin support vector machines for nonlinear classification[J].Neurocomputing, 2014, 130: 3-9.
[4] WU D, SHAO L. Multi-max-margin support vector machine for multi-source human action recognition[J]. Neurocomputing, 2014, 127: 98-103.
[5] 林浩, 李雷孝, 王慧. 支持向量机在智能交通系统中的研究应用综述[J]. 计算机科学与探索, 2020, 14(6): 901-917.
LIN H, LI L X, WANG H. Survey on research and application of support vector machines in intelligent transportation system[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(6): 901-917.
[6] 程凤伟, 王文剑. 基于近邻传输的粒度SVM算法[J]. 计算机科学与探索, 2020, 14(7): 1194-1199.
CHENG F W, WANG W J. Granular support vector machine algorithm based on affinity propagation[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(7): 1194-1199.
[7] 何丽, 韩克平, 刘颖. 自适应的SVM增量算法[J]. 计算机科学与探索, 2019, 13(4): 647-656.
HE L, HAN K P, LIU Y. Self-adaptive SVM incremental learning algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(4): 647-656.
[8] SUYKENS J, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.
[9] ZHANG Y, YAN S, QIAN X, et al. A fault diagnosis based on LSSVM and Bayesian probability for wind turbines[C]//Proceedings of the 2020 39th Chinese Control Conference,Shenyang, Jul 27-29, 2020. Piscataway: IEEE, 2020: 4101-4106.
[10] XIU L, NING W. RNA-GA with whale search based LSSVMs for modeling multivariable systems[C]//Proceedings of the 37th Chinese Control Conference, Wuhan, Jul 25-27, 2018.Piscataway: IEEE, 2018: 739-744.
[11] RAZAK I, ABIDIN I Z, SIAH Y K, et al. An optimization method of genetic algorithm for LSSVM in medium term electricity price forecasting[J]. Journal of Telecommunication, Electronic and Computer Engineering, 2018, 10: 99-103.
[12] YU C, XI Z, LU Y, et al. LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 199(3): 103956.
[13] 杨建新, 兰小平, 姚志强, 等. 基于郊狼算法优化的LSSVM多工序质量预测方法[J]. 制造业自动化, 2021, 43(12):105-109.
YANG J X, LAN X P, YAO Z Q, et al. Multi-process product quality prediction based on LSSVM optimized by coyote optimization algorithm[J]. Manufacturing Automation, 2021, 43(12): 105-109.
[14] BURGES C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[15] JAYADEVA. Learning a hyperplane classifier by minimizing an exact bound on the VC dimension[J]. Neurocomputing, 2014, 149: 683-689.
[16] 林建忠. 金融信息分析[M]. 上海: 上海交通大学出版社, 2015.
LIN J Z. Financial information analysis[M]. Shanghai: Shanghai Jiaotong University Press, 2015.
[17] 刘春. 基于PSO-LSSVM的网络流量预测模型[J]. 计算机系统应用, 2014, 23(10): 147-151.
LIU C. Network traffic prediction method based on particle swarm algorithm optimizing least square support vector machine[J]. Computer Systems & Applications, 2014, 23(10): 147-151. |