A new algorithm for support vector regression with automatic selection of hyperparameters

[1]  Yu Zhang,et al.  Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine , 2022, IEEE Journal of Biomedical and Health Informatics.

[2]  Essam H. Houssein,et al.  An efficient equilibrium optimizer with support vector regression for stock market prediction , 2021, Neural Computing and Applications.

[3]  Carlos H. Llanos,et al.  Multi-objective adaptive differential evolution for SVM/SVR hyperparameters selection , 2021, Pattern Recognit..

[4]  Alwyn R. Pais,et al.  A heuristic technique to detect phishing websites using TWSVM classifier , 2020, Neural Computing and Applications.

[5]  Witold Pedrycz,et al.  Robust twin support vector regression based on rescaled Hinge loss , 2020, Pattern Recognit..

[6]  Amir H. Gandomi,et al.  Marine Predators Algorithm: A nature-inspired metaheuristic , 2020, Expert Syst. Appl..

[7]  You‐Gan Wang,et al.  A working likelihood approach for robust regression , 2020, Statistical methods in medical research.

[8]  Seyedali Mirjalili,et al.  Equilibrium optimizer: A novel optimization algorithm , 2020, Knowl. Based Syst..

[9]  Chih-Jen Lin,et al.  Parameter Selection for Linear Support Vector Regression , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Seyedali Mirjalili,et al.  Henry gas solubility optimization: A novel physics-based algorithm , 2019, Future Gener. Comput. Syst..

[11]  Umesh Gupta,et al.  An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function , 2019, Applied Intelligence.

[12]  Ling Jing,et al.  Discriminative information-based nonparallel support vector machine , 2019, Signal Process..

[13]  Yunan Wu,et al.  A Survey of Tuning Parameter Selection for High-dimensional Regression , 2019, Annual Review of Statistics and Its Application.

[14]  Suresh Chandra,et al.  A ν-twin support vector machine based regression with automatic accuracy control , 2017, Applied Intelligence.

[15]  Xianli Pan,et al.  A Novel Twin Support-Vector Machine With Pinball Loss , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Jonathan T. Barron,et al.  A General and Adaptive Robust Loss Function , 2017, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Bin Li,et al.  Improving Efficiency of SVM k-Fold Cross-Validation by Alpha Seeding , 2016, AAAI.

[18]  S. Balasundaram,et al.  On optimization based extreme learning machine in primal for regression and classification by functional iterative method , 2016, Int. J. Mach. Learn. Cybern..

[19]  Reshma Khemchandani,et al.  Improvements on ν-Twin Support Vector Machine , 2016, Neural Networks.

[20]  Hiromasa Kaneko,et al.  Fast optimization of hyperparameters for support vector regression models with highly predictive ability , 2015 .

[21]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[22]  Xinjun Peng,et al.  TSVR: An efficient Twin Support Vector Machine for regression , 2010, Neural Networks.

[23]  Z. Bai,et al.  Robust Estimation Using the Huber Function With a Data-Dependent Tuning Constant , 2007 .

[24]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

[25]  B. M. Brown,et al.  Standard errors and covariance matrices for smoothed rank estimators , 2005 .

[26]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[27]  R. Tibshirani,et al.  1-norm Support Vector Machines , 2003, NIPS.

[28]  Shun-Feng Su,et al.  Support vector interval regression networks for interval regression analysis , 2003, Fuzzy Sets Syst..

[29]  Ryohei Nakano,et al.  Optimizing Support Vector regression hyperparameters based on cross-validation , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[30]  Chih-Jen Lin,et al.  Training v-Support Vector Regression: Theory and Algorithms , 2002, Neural Computation.

[31]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[32]  Bernhard Schölkopf,et al.  Shrinking the Tube: A New Support Vector Regression Algorithm , 1998, NIPS.

[33]  R. C. Williamson,et al.  Support vector regression with automatic accuracy control. , 1998 .

[34]  S. Lipsitz,et al.  Performance of generalized estimating equations in practical situations. , 1994, Biometrics.

[35]  Xin Liu,et al.  Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm , 2020, IEEE Access.

[36]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[37]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .