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Spline function smooth support vector machine for classification
1. | School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, 610054, China, China |
2. | Accounting & Information Systems, Virginia Polytechnic Institute and State University, VA, 24061, United States |
[1] |
Jian Luo, Shu-Cherng Fang, Yanqin Bai, Zhibin Deng. Fuzzy quadratic surface support vector machine based on fisher discriminant analysis. Journal of Industrial and Management Optimization, 2016, 12 (1) : 357-373. doi: 10.3934/jimo.2016.12.357 |
[2] |
Ahmad Mousavi, Zheming Gao, Lanshan Han, Alvin Lim. Quadratic surface support vector machine with L1 norm regularization. Journal of Industrial and Management Optimization, 2022, 18 (3) : 1835-1861. doi: 10.3934/jimo.2021046 |
[3] |
Qianru Zhai, Ye Tian, Jingyue Zhou. A SMOTE-based quadratic surface support vector machine for imbalanced classification with mislabeled information. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2021230 |
[4] |
Yubo Yuan. Canonical duality solution for alternating support vector machine. Journal of Industrial and Management Optimization, 2012, 8 (3) : 611-621. doi: 10.3934/jimo.2012.8.611 |
[5] |
Ying Lin, Qi Ye. Support vector machine classifiers by non-Euclidean margins. Mathematical Foundations of Computing, 2020, 3 (4) : 279-300. doi: 10.3934/mfc.2020018 |
[6] |
Xin Li, Ziguan Cui, Linhui Sun, Guanming Lu, Debnath Narayan. Research on iterative repair algorithm of Hyperchaotic image based on support vector machine. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1199-1218. doi: 10.3934/dcdss.2019083 |
[7] |
Fatemeh Bazikar, Saeed Ketabchi, Hossein Moosaei. Smooth augmented Lagrangian method for twin bounded support vector machine. Numerical Algebra, Control and Optimization, 2021 doi: 10.3934/naco.2021027 |
[8] |
Xin Yan, Hongmiao Zhu. A kernel-free fuzzy support vector machine with Universum. Journal of Industrial and Management Optimization, 2021 doi: 10.3934/jimo.2021184 |
[9] |
Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1267-1276. doi: 10.3934/dcdss.2015.8.1267 |
[10] |
Huiqin Zhang, JinChun Wang, Meng Wang, Xudong Chen. Integration of cuckoo search and fuzzy support vector machine for intelligent diagnosis of production process quality. Journal of Industrial and Management Optimization, 2022, 18 (1) : 195-217. doi: 10.3934/jimo.2020150 |
[11] |
Ye Tian, Wei Yang, Gene Lai, Menghan Zhao. Predicting non-life insurer's insolvency using non-kernel fuzzy quadratic surface support vector machines. Journal of Industrial and Management Optimization, 2019, 15 (2) : 985-999. doi: 10.3934/jimo.2018081 |
[12] |
K. Schittkowski. Optimal parameter selection in support vector machines. Journal of Industrial and Management Optimization, 2005, 1 (4) : 465-476. doi: 10.3934/jimo.2005.1.465 |
[13] |
Pooja Louhan, S. K. Suneja. On fractional vector optimization over cones with support functions. Journal of Industrial and Management Optimization, 2017, 13 (2) : 549-572. doi: 10.3934/jimo.2016031 |
[14] |
Florian Dumpert. Quantitative robustness of localized support vector machines. Communications on Pure and Applied Analysis, 2020, 19 (8) : 3947-3956. doi: 10.3934/cpaa.2020174 |
[15] |
Hong-Gunn Chew, Cheng-Chew Lim. On regularisation parameter transformation of support vector machines. Journal of Industrial and Management Optimization, 2009, 5 (2) : 403-415. doi: 10.3934/jimo.2009.5.403 |
[16] |
Qinglei Zhang, Wenying Feng. Detecting coalition attacks in online advertising: A hybrid data mining approach. Big Data & Information Analytics, 2016, 1 (2&3) : 227-245. doi: 10.3934/bdia.2016006 |
[17] |
Zhen Mei. Manifold data mining helps businesses grow more effectively. Big Data & Information Analytics, 2016, 1 (2&3) : 275-276. doi: 10.3934/bdia.2016009 |
[18] |
Sunmoo Yoon, Maria Patrao, Debbie Schauer, Jose Gutierrez. Prediction models for burden of caregivers applying data mining techniques. Big Data & Information Analytics, 2017 doi: 10.3934/bdia.2017014 |
[19] |
Anupama N, Sudarson Jena. A novel approach using incremental under sampling for data stream mining. Big Data & Information Analytics, 2018 doi: 10.3934/bdia.2017017 |
[20] |
Siamak RabieniaHaratbar. Support theorem for the Light-Ray transform of vector fields on Minkowski spaces. Inverse Problems and Imaging, 2018, 12 (2) : 293-314. doi: 10.3934/ipi.2018013 |
2020 Impact Factor: 1.801
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