[1]
|
V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer Verlag, New York, 2000.
|
[2]
|
H. Fröhlich and O. Chapelle, “Feature Selection for Support Vector Machines by Means of Genetic Algorithms,” Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, 3-5 November 2003, pp. 142-148.
|
[3]
|
C. W. Hsu and C. J. Lin, “A Simple Decomposition Method for Support Vector Machine,” Machine Learning, Vol. 46, No. 3, 2002, pp. 219-314.
|
[4]
|
H. Liu and H. Motoda, “Feature Selection for Knowledge Discovery and Data Mining,” Kluwer Academic, Boston, 1998.
|
[5]
|
R. C. Chen and C. H. Hsieh, “Web Page Classification Based on a Support Vector Machine Using a Weighed Vote Schema,” Expert Systems with Applications, Vol. 31, No. 2, 2006, pp. 427-435.
|
[6]
|
C. Gold, A. Holub and P. Sollich, “Bayesian Approach to Feature Selection and Parameter Tuning for Support Vector Machine Classifiers,” Neural Networks, Vol. 18, No. 5-6, 2005, pp. 693-701.
|
[7]
|
R. Kohavi and G. H. John, “Wrappers for Feature Subset Selection,” Artificial Intelligence, Vol. 97, No. 1-2, 1997, pp. 273-324.
|
[8]
|
T. Shon, Y. Kim and J. Moon, “A Machine Learning Framework for Network Anomaly Detection Using SVM and GA,” Proceedings of 3rd IEEE International Workshop on Information Assurance and Security, 23-24 March 2005, pp. 176-183.
|
[9]
|
L. Zhang, L. Jack and A. K. Nandi, “Fault Detection Using Genetic Programming,” Mechanical Systems and Signal Processing, Vol. 19, No. 2, 2005, pp. 271-289.
|
[10]
|
B. Samanta, K. R. Al-Balushi and S. A. Al-Araimi, “Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection,” Engineering Applications of Artificial Intelligence, Vol. 16, No. 7-8, 2003, pp. 657-665.
|
[11]
|
C. L. Huang, M. C. Chen and C. J. Wang, “Credit Scoring with a Data Mining Approach Based on Support Vector Machines,” Expert Systems with Applications, Vol. 33, No. 4, 2007, pp 847-856.
|
[12]
|
C. L. Huang and C. L. Wang, “A GA-Based Feature Selection and Parameters Optimization for Support Vector Machines,” Expert Systems with Applications, Vol. 31, No. 2, 2006, pp. 231-240.
|
[13]
|
C. W. Hsu, C. C. Chang and C. J. Lin, “A Practical Guide to Support Vector Classification,” Technical Report, Department of Computer Science and Information Engineering, University of National Taiwan, Taipei, 2003, pp. 1-12.
|
[14]
|
P. F. Pai and W. C. Hong, “Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting,” Energy Conversion and Management, Vol. 46, No. 17, 2005, pp. 2669-2688.
|
[15]
|
F. Melgani and L. Bruzzone, “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, 2004, pp. 1778-1790.
|
[16]
|
G. M. Foody and A. A. Mathur, “Relative Evaluation of Multiclass Image Classification by Support Vector Machines,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 6, 2004, pp. 1335-1343.
|
[17]
|
J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, IEEE Neural Networks Society, Perth, 27 November-1 December 1995, pp. 1942-1948.
|
[18]
|
S. Hettich, C. L. Blake and C. J. Merz, “UCI Repository of Machine Learning Databases,” Department of Information and Computer Science, University of California, Irvine, 1998. http//www.ics.uci.edu/~mlearn/MLRepository.html
|
[19]
|
“Aviris Indiana’s IndianPinesl DataSet.” ftp://ftp.ecn.Purdue.edu/biehl/MultiSpec/92AV3C.lan; ftp://ftp.ecn.purdue. edu/biehl/PCMultiSpeeThyFiles.zip
|
[20]
|
C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” 2005. http://www.csie.ntu.edu. tw/~cjlin/libsvm
|