Multi-Objective Parameter Selection for Classifiers
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Ludwig Lausser | Hans A. Kestler | Markus Maucher | Christoph Müssel | H. Kestler | L. Lausser | Christoph Müssel | Markus Maucher
[1] A. E. Eiben,et al. Introduction to Evolutionary Computing , 2003, Natural Computing Series.
[2] Alex N. Kalos. Automated neural network structure determination via discrete particle swarm optimization (for non-linear time series models) , 2005 .
[3] Hans-Paul Schwefel,et al. Evolution strategies – A comprehensive introduction , 2002, Natural Computing.
[4] Harald Niederreiter,et al. Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.
[5] R. Caflisch,et al. Quasi-Monte Carlo integration , 1995 .
[6] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[7] Christian Igel,et al. Multi-Objective Optimization of Support Vector Machines , 2006, Multi-Objective Machine Learning.
[8] Robert Sabourin,et al. A PSO-based framework for dynamic SVM model selection , 2009, GECCO '09.
[9] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[10] Max A. Little,et al. Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.
[11] Thomas Bartz-Beielstein,et al. Sequential parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.
[12] Massimo Pappalardo,et al. Multiobjective Optimization: A Brief Overview , 2008 .
[13] Ron Kohavi,et al. Automatic Parameter Selection by Minimizing Estimated Error , 1995, ICML.
[14] Richard Simon,et al. Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.
[15] Anne-Laure Boulesteix,et al. Bmc Medical Research Methodology Open Access Optimal Classifier Selection and Negative Bias in Error Rate Estimation: an Empirical Study on High-dimensional Prediction , 2022 .
[16] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[17] Bernd Bischl,et al. Resampling Methods in Model Validation , 2010 .
[18] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[19] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[20] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[21] Kalyanmoy Deb,et al. Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.
[22] H. Niederreiter. Low-discrepancy and low-dispersion sequences , 1988 .
[23] Hisao Ishibuchi,et al. Evolutionary many-objective optimization: A short review , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[24] Massimiliano Pontil,et al. Properties of Support Vector Machines , 1998, Neural Computation.
[25] Heike Trautmann,et al. Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions , 2010, IEEE Transactions on Evolutionary Computation.
[26] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[27] Theodor J. Stewart,et al. Multiple criteria decision analysis - an integrated approach , 2001 .
[28] Guido Schwarzer,et al. Easier parallel computing in R with snowfall and sfCluster , 2009, R J..
[29] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Multiclass SVM Model Selection Using Particle Swarm Optimization , 2006, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06).
[30] Thomas Bartz-Beielstein,et al. Experimental Research in Evolutionary Computation - The New Experimentalism , 2010, Natural Computing Series.
[31] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[32] Xiusheng Duan,et al. Parameters optimization of Support Vector Machine based on Simulated Annealing and Genetic Algorithm , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[33] 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..
[34] Christian Igel,et al. Multi-objective Model Selection for Support Vector Machines , 2005, EMO.
[35] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.
[36] Gábor Csárdi,et al. The igraph software package for complex network research , 2006 .
[37] M. D. McKay,et al. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .
[38] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[39] Jiao Licheng,et al. Automatic parameters selection for SVM based on GA , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).
[40] Yingqin Zhang. Evolutionary Computation Based Automatic SVM Model Selection , 2008, 2008 Fourth International Conference on Natural Computation.
[41] Paul Bratley,et al. Algorithm 659: Implementing Sobol's quasirandom sequence generator , 1988, TOMS.