A geometric method for model selection in support vector machine
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Science Applications,General Engineering
Reference12 articles.
1. Optimizing resources in model selection for support vector machine;Adankon;Pattern Recognition,2007
2. Automatic model selection for the optimization of the SVM kernels;Ayat;Pattern Recognition Computer Science,2005
3. Choosing multiple parameters for support vector machines;Chapelle;Machine Learning,2002
4. An introduction to support vector machines;Christianini,2002
5. Radius margin bounds for support vector machines with the RBF kernel;Chung;Neural Computation,2003
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1. A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm;Neural Computing and Applications;2017-03-13
2. Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets;TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES;2016
3. A new fuzzy membership assignment and model selection approach based on dynamic class centers for fuzzy SVM family using the firefly algorithm;TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES;2016
4. AN EFFICIENT MODEL SELECTION FOR SVM IN REAL-WORLD DATASETS USING BGA AND RGA;Neural Network World;2014
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