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Support vector machines with adaptive Lq penalty

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  • Liu, Yufeng
  • Helen Zhang, Hao
  • Park, Cheolwoo
  • Ahn, Jeongyoun

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  • Liu, Yufeng & Helen Zhang, Hao & Park, Cheolwoo & Ahn, Jeongyoun, 2007. "Support vector machines with adaptive Lq penalty," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6380-6394, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:6380-6394
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    4. Antoniadis A. & Fan J., 2001. "Regularization of Wavelet Approximations," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 939-967, September.
    5. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    Cited by:

    1. Guan, Wei & Gray, Alexander, 2013. "Sparse high-dimensional fractional-norm support vector machine via DC programming," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 136-148.
    2. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    3. Ze Han & Wei Song & Xiangzheng Deng, 2016. "Responses of Ecosystem Service to Land Use Change in Qinghai Province," Energies, MDPI, vol. 9(4), pages 1-16, April.
    4. Hoai An Le Thi & Manh Cuong Nguyen, 2017. "DCA based algorithms for feature selection in multi-class support vector machine," Annals of Operations Research, Springer, vol. 249(1), pages 273-300, February.
    5. Lianhui Li & Chunyang Mu & Shaohu Ding & Zheng Wang & Runyang Mo & Yongfeng Song, 2015. "A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination," Energies, MDPI, vol. 9(1), pages 1-22, December.
    6. Huang, Wei & Yang, Kailin & Ma, Jiming & Xu, Yaowu & Guo, Xinlei & Wang, Jue, 2018. "A new setting criterion of tailrace surge chambers for pumped-storage power plants," Renewable Energy, Elsevier, vol. 116(PA), pages 194-201.
    7. Wanying Song & Jian Min & Jianbo Yang, 2023. "Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    8. De Brabanter, K. & De Brabanter, J. & Suykens, J.A.K. & De Moor, B., 2010. "Optimized fixed-size kernel models for large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1484-1504, June.
    9. Luis M. Briceño-Arias & Giovanni Chierchia & Emilie Chouzenoux & Jean-Christophe Pesquet, 2019. "A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression," Computational Optimization and Applications, Springer, vol. 72(3), pages 707-726, April.
    10. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.

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