1 |
Chen, J., Zhang, W., Yu, Z., and Feng, Z. (2013), Automated Spot Weld Inspection using Infrared Thermography, In Trends in Welding Research 2012 : Proceedings of the 9th International Conference, 148-151.
|
2 |
Choi, H. H., Lee, G. H., Kim, J. G., Joo, Y. B., Choi, B. J., Park, K. H., and Yun, B. J. (2009), A Defect Inspection Method in TFT-LCD Panel Using LS-SVM, Journal of Korean Institute of Intelligent Systems, 19(6), 852-859.
DOI
|
3 |
Cristianini, N. and Shawe-Taylor, J. (2000), An introduction to support vector machines and other kernel-based learning methods, Cambridge university press, 149-160.
|
4 |
Hansson, K., Yella, S., Dougherty, M., and Fleyeh, H. (2016), Machine Learning Algorithms in Heavy Process Manufacturing, American Journal of Intelligent Systems, 6(1), 1-13.
|
5 |
Hsu, C. W. and Lin, C. J. (2002), A comparison of methods for multiclass support vector machines, Neural Networks, IEEE Transactions on, 13(2), 415-425.
DOI
|
6 |
Jang, D.-Y. and Bae, S.-J. (2009), Hybrid Datamining Algorithm for Monitoring Input Variables in Semiconductor Manufacturing Process, In Conference of the Korean Institute of Industrial Engineers , 563-569.
|
7 |
Jung, H. S. (2009), An empirical study on the characteristics of the correlations between industry indexes in the Korean stock market, Korea Advanced Institute of Science and Technology, KGSF-Theses_Master.
|
8 |
Kadlec, P., Gabrys, B., and Strandt, S. (2009), Data-driven soft sensors in the process industry, Computers and Chemical Engineering, 33(4), 795-814.
DOI
|
9 |
Kim, G. S., Lee, S., and Cho, J. S. (2013), A Learning-based Visual Inspection System for Part Verification in a Panorama Sunroof Assembly Line using the SVM Algorithm, Journal of Institute of Control, Robotics and System, 19(12), 1099-1104s.
DOI
|
10 |
Kim, S. J., Seo, I. Y., and Shin, H. C. (2008), An SVM based On-line Monitoring for Sensor Calibrations in the Nuclear Power Plant, Journal of the Korean Institute of Industrial Engineers, 5, 1137-1145.
|
11 |
Lee, J., Kao, H. A., and Yang, S. (2014), Service innovation and smart analytics for industry 4.0 and big data environment, Procedia CIRP, 16, 3-8.
DOI
|
12 |
Mukaka, M. M. (2012), A guide to appropriate use of Correlation coefficient in medical research, Malawi Medical Journal, 24(3), 69-71.
|
13 |
Oh, Y. G., Park, H. S., Yoo, A., Kim, N. H., Kim, Y. H., Kim, D. C., Choi, J. U., Yoon, S. H., and Yang, H. J. (2013), A Product Quality Prediction Model Using Real-Time Process Monitoring in Manufacturing Supply Chain, Journal of the Korean Institute of Industrial Engineers, 39(4), 271-277.
DOI
|
14 |
Chang, C. C. and Lin, C. J. (2011), LIBSVM : a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
|
15 |
Chen, Y. W. and Lin, C. J. (2006), Combining SVMs with various feature selection strategies, In Feature extraction. Springer Berlin Heidelberg, 315-324.
|
16 |
Ngai, E. W. T., Peng, S., Alexander, P., and Moon, K. K. (2014), Decision support and intelligent systems in the textile and apparel supply chain : An academic review of research articles, Expert Systems with Applications, 41(1), 81-91.
DOI
|
17 |
Oh, Y. G., Ju, I. C., Lee, W. Y., and Kim, N. H. (2015), Modeling and Implementation of the Affordance-based Human-Machine Collaborative System, Journal of the Korean Institute of Industrial Engineers, 41(1), 34-42
DOI
|
18 |
O'Rourke, N. and Hatcher, L. (2013), A step-by-step approach to using SAS for factor analysis and structural equation modeling, Sas Institute, 379-392.
|
19 |
Pal, M. and Foody, G. M. (2010), Feature selection for classification of hyperspectral data by SVM, IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297-2307.
DOI
|
20 |
Vapnik, V. (2013), The nature of statistical learning theory, Springer Science and Business Media, 181-223.
|
21 |
Widodo, A. and Yang, B. S. (2007), Support vector machine in machine condition monitoring and fault diagnosis. Mechanical systems and signal processing, 21(6), 2560-2574.
DOI
|
22 |
Yu, L. and Liu, H. (2004), Efficient feature selection via analysis of relevance and redundancy, The Journal of Machine Learning Research, 5, 1205-1224.
|
23 |
Yu, L. and Liu, H. (2003), Feature selection for high-dimensional data : A fast correlation-based filter solution, Paper presented at the ICML.
|