How to cite this paper
Xiao, J., Liu, G., Huang, M., Yin, Z & Gao, Z. (2023). A kernel-free L1 norm regularized ν-support vector machine model with application.International Journal of Industrial Engineering Computations , 14(4), 691-706.
Refrences
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Cabrera, J., Dionisio, A., & Solano, G. (2015, July). Lung cancer classification tool using microarray data and support vector machines. In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-6). IEEE.
Cholette, M. E., Borghesani, P., Di Gialleonardo, E., & Braghin, F. (2017). Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications. Expert Systems with Applications, 81, 39-52.
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Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
Dagher, I. (2008). Quadratic kernel-free non-linear support vector machine. Journal of Global Optimization, 41(1), 15-30.
Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert systems with applications, 42(6), 3194-3204.
Di, M., & Joo, E. M. (2007, December). A survey of machine learning in wireless sensor netoworks from networking and application perspectives. In 2007 6th international conference on information, communications & signal processing (pp. 1-5). IEEE.
Gao, Q. Q., Bai, Y. Q., & Zhan, Y. R. (2019). Quadratic kernel-free least square twin support vector machine for binary classification problems. Journal of the Operations Research Society of China, 7, 539-559.
Gao, Z., Fang, S. C., Luo, J., & Medhin, N. (2021). A kernel-free double well potential support vector machine with applications. European Journal of Operational Research, 290(1), 248-262.
Gao, Z., Wang, Y., Huang, M., Luo, J., & Tang, S. (2022). A kernel-free fuzzy reduced quadratic surface ν-support vector machine with applications. Applied Soft Computing, 127, 109390.
Hsu, Y. H., & Si, D. (2018, July). Cancer type prediction and classification based on rna-sequencing data. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5374-5377). IEEE.
Lin, C. F., & Wang, S. D. (2002). Fuzzy support vector machines. IEEE transactions on neural networks, 13(2), 464-471.
Luo, J., Fang, S. C., Bai, Y., & Deng, Z. (2016). Fuzzy quadratic surface support vector machine based on fisher discriminant analysis. Journal of Industrial and Management Optimization, 12(1), 357-373.
Luo, J., Fang, S. C., Deng, Z., & Guo, X. (2016). Soft quadratic surface support vector machine for binary classification. Asia-Pacific Journal of Operational Research, 33(06), 1650046.
Luo, J., Tian, Y., & Yan, X. (2017). Clustering via fuzzy one-class quadratic surface support vector machine. Soft Computing, 21, 5859-5865.
Mainali, S., Darsie, M. E., & Smetana, K. S. (2021). Machine learning in action: stroke diagnosis and outcome prediction. Frontiers in Neurology, 12, 734345.
Malik, A. K., Ganaie, M. A., Tanveer, M., Suganthan, P. N., & Alzheimer's Disease Neuroimaging Initiative Initiative. (2022). Alzheimer's disease diagnosis via intuitionistic fuzzy random vector functional link network. IEEE Transactions on Computational Social Systems.
Mousavi, A., Gao, Z., Han, L., & Lim, A. (2019). Quadratic surface support vector machine with L1 norm regularization. arXiv preprint arXiv:1908.08616.
Mousavi, A., Rezaee, M., & Ayanzadeh, R. (2019). A survey on compressive sensing: Classical results and recent advancements. arXiv preprint arXiv:1908.01014.
Mousavi, S., & Shen, J. (2019). Solution uniqueness of convex piecewise affine functions based optimization with applications to constrained ℓ1 minimization. ESAIM: Control, Optimisation and Calculus of Variations, 25, 56.
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New support vector algorithms. Neural computation, 12(5), 1207-1245.
Shen, J., & Mousavi, S. (2018). Least Sparsity of p-Norm Based Optimization Problems with p>1. SIAM Journal on Optimization, 28(3), 2721-2751.
Tang, S., Cao, P., Huang, M., Liu, X., & Zaiane, O. (2022). Dual feature correlation guided multi-task learning for Alzheimer's disease prediction. Computers in Biology and Medicine, 140, 105090.
Tian, Y., Yong, Z., & Luo, J. (2018). A new approach for reject inference in credit scoring using kernel-free fuzzy quadratic surface support vector machines. Applied Soft Computing, 73, 96-105.
Xie, Y., Meng, W. Y., Li, R. Z., Wang, Y. W., Qian, X., Chan, C., ... & Leung, E. L. H. (2021). Early lung cancer diagnostic biomarker discovery by machine learning methods. Translational oncology, 14(1), 100907.
Yan, H. S., & Xu, D. (2007). An Approach to Estimating Product Design Time Based on Fuzzy $\nu $-Support Vector Machine. IEEE transactions on Neural Networks, 18(3), 721-731.
Yan, X., Bai, Y., Fang, S. C., & Luo, J. (2018). A proximal quadratic surface support vector machine for semi-supervised binary classification. Soft Computing, 22, 6905-6919.
Yu, Z., Wang, Z., Yu, X., & Zhang, Z. (2020). RNA-seq-based breast cancer subtypes classification using machine learning approaches. Computational intelligence
Zhou, Z. H. (2021). Machine learning. Springer Nature.
Cabrera, J., Dionisio, A., & Solano, G. (2015, July). Lung cancer classification tool using microarray data and support vector machines. In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-6). IEEE.
Cholette, M. E., Borghesani, P., Di Gialleonardo, E., & Braghin, F. (2017). Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications. Expert Systems with Applications, 81, 39-52.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
Dagher, I. (2008). Quadratic kernel-free non-linear support vector machine. Journal of Global Optimization, 41(1), 15-30.
Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert systems with applications, 42(6), 3194-3204.
Di, M., & Joo, E. M. (2007, December). A survey of machine learning in wireless sensor netoworks from networking and application perspectives. In 2007 6th international conference on information, communications & signal processing (pp. 1-5). IEEE.
Gao, Q. Q., Bai, Y. Q., & Zhan, Y. R. (2019). Quadratic kernel-free least square twin support vector machine for binary classification problems. Journal of the Operations Research Society of China, 7, 539-559.
Gao, Z., Fang, S. C., Luo, J., & Medhin, N. (2021). A kernel-free double well potential support vector machine with applications. European Journal of Operational Research, 290(1), 248-262.
Gao, Z., Wang, Y., Huang, M., Luo, J., & Tang, S. (2022). A kernel-free fuzzy reduced quadratic surface ν-support vector machine with applications. Applied Soft Computing, 127, 109390.
Hsu, Y. H., & Si, D. (2018, July). Cancer type prediction and classification based on rna-sequencing data. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5374-5377). IEEE.
Lin, C. F., & Wang, S. D. (2002). Fuzzy support vector machines. IEEE transactions on neural networks, 13(2), 464-471.
Luo, J., Fang, S. C., Bai, Y., & Deng, Z. (2016). Fuzzy quadratic surface support vector machine based on fisher discriminant analysis. Journal of Industrial and Management Optimization, 12(1), 357-373.
Luo, J., Fang, S. C., Deng, Z., & Guo, X. (2016). Soft quadratic surface support vector machine for binary classification. Asia-Pacific Journal of Operational Research, 33(06), 1650046.
Luo, J., Tian, Y., & Yan, X. (2017). Clustering via fuzzy one-class quadratic surface support vector machine. Soft Computing, 21, 5859-5865.
Mainali, S., Darsie, M. E., & Smetana, K. S. (2021). Machine learning in action: stroke diagnosis and outcome prediction. Frontiers in Neurology, 12, 734345.
Malik, A. K., Ganaie, M. A., Tanveer, M., Suganthan, P. N., & Alzheimer's Disease Neuroimaging Initiative Initiative. (2022). Alzheimer's disease diagnosis via intuitionistic fuzzy random vector functional link network. IEEE Transactions on Computational Social Systems.
Mousavi, A., Gao, Z., Han, L., & Lim, A. (2019). Quadratic surface support vector machine with L1 norm regularization. arXiv preprint arXiv:1908.08616.
Mousavi, A., Rezaee, M., & Ayanzadeh, R. (2019). A survey on compressive sensing: Classical results and recent advancements. arXiv preprint arXiv:1908.01014.
Mousavi, S., & Shen, J. (2019). Solution uniqueness of convex piecewise affine functions based optimization with applications to constrained ℓ1 minimization. ESAIM: Control, Optimisation and Calculus of Variations, 25, 56.
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New support vector algorithms. Neural computation, 12(5), 1207-1245.
Shen, J., & Mousavi, S. (2018). Least Sparsity of p-Norm Based Optimization Problems with p>1. SIAM Journal on Optimization, 28(3), 2721-2751.
Tang, S., Cao, P., Huang, M., Liu, X., & Zaiane, O. (2022). Dual feature correlation guided multi-task learning for Alzheimer's disease prediction. Computers in Biology and Medicine, 140, 105090.
Tian, Y., Yong, Z., & Luo, J. (2018). A new approach for reject inference in credit scoring using kernel-free fuzzy quadratic surface support vector machines. Applied Soft Computing, 73, 96-105.
Xie, Y., Meng, W. Y., Li, R. Z., Wang, Y. W., Qian, X., Chan, C., ... & Leung, E. L. H. (2021). Early lung cancer diagnostic biomarker discovery by machine learning methods. Translational oncology, 14(1), 100907.
Yan, H. S., & Xu, D. (2007). An Approach to Estimating Product Design Time Based on Fuzzy $\nu $-Support Vector Machine. IEEE transactions on Neural Networks, 18(3), 721-731.
Yan, X., Bai, Y., Fang, S. C., & Luo, J. (2018). A proximal quadratic surface support vector machine for semi-supervised binary classification. Soft Computing, 22, 6905-6919.
Yu, Z., Wang, Z., Yu, X., & Zhang, Z. (2020). RNA-seq-based breast cancer subtypes classification using machine learning approaches. Computational intelligence
Zhou, Z. H. (2021). Machine learning. Springer Nature.