A nested heuristic for parameter tuning in Support Vector Machines
暂无分享,去创建一个
Emilio Carrizosa | Dolores Romero Morales | Belen Martin-Barragan | E. Carrizosa | D. Morales | B. Martín-Barragán
[1] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[2] Samy Bengio,et al. SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..
[3] Nenad Mladenovic,et al. A continuous variable neighborhood search heuristic for finding the three-dimensional structure of a molecule , 2008, Eur. J. Oper. Res..
[4] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[5] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[6] LocatelliMarco,et al. Efficient Algorithms for Large Scale Global Optimization , 2003 .
[7] Ronny Luss,et al. Mathematical programming for statistical learning with applications in biology and finance , 2009 .
[8] Pierre Hansen,et al. Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem , 2000, Oper. Res..
[9] V. Vapnik,et al. Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.
[10] Gunnar Rätsch,et al. Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..
[11] S. Sathiya Keerthi,et al. Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.
[12] A. Zell,et al. Efficient parameter selection for support vector machines in classification and regression via model-based global optimization , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[13] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[14] Mirjana Cangalovic,et al. Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search , 2003, Eur. J. Oper. Res..
[15] Nenad Mladenović,et al. A Variable Neighbourhood Algorithm for Solving the Continuous Location-Allocation Problem , 1995 .
[16] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[17] Emilio Carrizosa,et al. Supervised classification and mathematical optimization , 2013, Comput. Oper. Res..
[18] Ethem Alpaydin,et al. Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..
[19] Carl Gold,et al. Model selection for support vector machine classification , 2002, Neurocomputing.
[20] Alexandre d'Aspremont,et al. Support vector machine classification with indefinite kernels , 2007, Math. Program. Comput..
[21] Fabio Schoen,et al. Fast Global Optimization of Difficult Lennard-Jones Clusters , 2002, Comput. Optim. Appl..
[22] Javier M. Moguerza,et al. Methods for the combination of kernel matrices within a support vector framework , 2009, Machine Learning.
[23] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[24] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Evolutionary tuning of SVM parameter values in multiclass problems , 2008, Neurocomputing.
[25] Nenad Mladenović,et al. GLOB — A new VNS-based Software for Global Optimization , 2006 .
[26] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[27] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[28] Thomas P. Trappenberg,et al. A Heuristic for Free Parameter Optimization with Support Vector Machines , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[29] C. C. Lau,et al. An Integrated Approach of Support Vector Machine and Variable Neighborhood Search for Discovering Combinational Gene Signatures in Predicting Chemo-response of Osteosarcoma , 2008 .
[30] Kristin P. Bennett,et al. A Pattern Search Method for Model Selection of Support Vector Regression , 2002, SDM.
[31] Christopher K. I. Williams. Learning Kernel Classifiers , 2003 .
[32] Pierre Hansen,et al. Variable Neighborhood Search , 2018, Handbook of Heuristics.
[33] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[34] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[35] Christian Igel,et al. Evolutionary tuning of multiple SVM parameters , 2005, ESANN.
[36] Andrea Grosso,et al. Solving molecular distance geometry problems by global optimization algorithms , 2009, Comput. Optim. Appl..
[37] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[38] Michael Rabadi,et al. Kernel Methods for Machine Learning , 2015 .
[39] Pierre Hansen,et al. Variable neighborhood search , 1997, Eur. J. Oper. Res..
[40] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[41] J. Paul Brooks,et al. Support Vector Machines with the Ramp Loss and the Hard Margin Loss , 2011, Oper. Res..
[42] Mirjana Cangalovic,et al. General variable neighborhood search for the continuous optimization , 2006, Eur. J. Oper. Res..
[43] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[44] Fabio Schoen,et al. Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters , 2003, Comput. Optim. Appl..
[45] Paul Davidsson,et al. Quantifying the Impact of Learning Algorithm Parameter Tuning , 2006, AAAI.
[46] Nelson Maculan,et al. A Function to Test Methods Applied to Global Minimization of Potential Energy of Molecules , 2004, Numerical Algorithms.
[47] Pierre Hansen,et al. Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..
[48] Ralf Herbrich,et al. Learning Kernel Classifiers: Theory and Algorithms , 2001 .
[49] Sheng-De Wang,et al. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space , 2009, Pattern Recognit..
[50] Xiaoli Zhang,et al. An ACO-based algorithm for parameter optimization of support vector machines , 2010, Expert Syst. Appl..
[51] José R. Dorronsoro,et al. Finding optimal model parameters by deterministic and annealed focused grid search , 2009, Neurocomputing.
[52] Nenad Mladenovic,et al. Gaussian variable neighborhood search for continuous optimization , 2011, Comput. Oper. Res..
[53] Antoine Geissbühler,et al. Model Selection for Support Vector Classifiers via Genetic Algorithms. An Application to Medical Decision Support , 2004, ISBMDA.