Optimization of SVM parameters using High Dimensional Model Representation and its application to hyperspectral images;Destek vektör makinalari model parametrelerinin yüksek boyutlu model gösterilimi ile optimizasyonu ve hiperspektral görüntü lere uygulanmasi

2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

Volumn , Issue , 2014, Pages 642-645

  Kaya, G Taşkin ^a   Kaya, H ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

high dimensional model representation; Model selection; optimization; support vector machines

Indexed keywords

FUNCTIONS; IMAGE RECONSTRUCTION; OPTIMIZATION; SIGNAL PROCESSING; SPECTROSCOPY;

CLASSIFICATION OF REMOTE SENSING IMAGE; CLASSIFICATION PERFORMANCE; HIGH DIMENSIONAL MODEL REPRESENTATION; HIGH-DIMENSIONAL SYSTEMS; HYPER-SPECTRAL IMAGES; MATHEMATICAL FUNCTIONS; MODEL SELECTION; SCIENCE AND ENGINEERING;

SUPPORT VECTOR MACHINES;

EID: 84903756405     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SIU.2014.6830311     Document Type: Conference Paper

References (13)

1. Marcelo N. Kapp, Robert Sabourin, and Patrick Maupin, "A dynamic model selection strategy for support vector machine classifiers", Applied Soft Computing, vol. 12, no. 8, pp. 2550-2565, Aug. 2012.
2. Chapelle, O. and Vladimir V., "Model Selection for Support Vector Machines", in Advances in Neural Information Processing Systems, 1999, number 1, pp. 230-236.
3. Villa, A., and Fauvel, M., and Chanussot, J., and Gamba, P. and Benediktsson J. A., "Gradient Optimization for multiple kernel's parameters in support vector machines classification", in IEEE International Geoscience and Remote Sensing Symposium, 2008, number 2, pp. IV - 224 - IV - 227.
4. Yuan Ren and Guangchen Bai, "Determination of Optimal SVM Parameters by Using GA/PSO", Journal of Computers, vol. 5, no. 8, pp. 1160-1168, Aug. 2010.
5. James Ethridge, Gregory Ditzler, and Robi Polikar, "Optimal-SVM Parameter Estimation using Multi Objective Evolutionary Algorithms", in IEEE Congress on Evolutionary Computation (CEC), 2010, pp. 1-8.
6. Weijian Kong, Weijian Cheng, Jinliang Ding, and Tianyou Chai, "A Reliable and Efficient Hybrid PSO for Parameters Optimization of LS-SVM in Production Rate Prediction", 2010 International Symposium on Computational Intelligence and Design, pp. 140-143, Oct. 2010.
7. Yakoub Bazi and Farid Melgani, "Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images", IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 11, pp. 3374-3385, 2006.
8. Cheng-Lung Huang and Chieh-Jen Wang, "A GA-based feature selection and parameters optimizationfor support vector machines", Expert Systems with Applications, vol. 31, no. 2, pp. 231-240, Aug. 2006.
9. Shutao Li and Mngkui Tan, "Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm", Neurocomputing, vol. 73, no. 10-12, pp. 2089-2096, June 2010.
10. Chin-teng Lin, Chin-Teng Lin, Bor-Chen Kuo, and Hui-Shan Chu, "An Automatic Method For Selecting The Parameter Of The RBF Kernel Function To Support Vector Machines Graduate Institute Of Educational Measurement And Statistics, National Taichung University", 2010.
11. I. M. Sobol, "Theorems and examples on high dimensional model representation", Reliability Engineering & System Safety, vol. 79, no. 2, pp. 187-193, Feb. 2003.
12. O. F. Alis, and H. Rabitz, "Efficient implementation of high dimensional model representations", Journal Of Mathematical Chemistry, vol. 29, no. 2, pp. 127-142, 2001.
13. H. Kaya, G. Taskin Kaya, and O. K. Ersoy, "Svm model selection by high dimensional model representation", in IGARSS, 2013.