| Reference: | Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., & Levine, A. J. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences, 96(12), 6745-6750.
Bellman, R. E. (2015). Adaptive Control Processes: A Guided Tour: Princeton University Press.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, USA.
Boulesteix, A.-L. (2004). PLS Dimension Reduction for Classification with Microarray Data Statistical applications in genetics and molecular biology (Vol. 3, pp. 1).
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018
Cunningham, P. (2008). Dimension Reduction. In M. Cord & P. Cunningham (Eds.), Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval (pp. 91-112). Berlin, Heidelberg: Springer Berlin Heidelberg.
Dai, J. J., Lieu, L., & Rocke, D. (2006). Dimension reduction for classification with gene expression microarray data. Statistical applications in genetics and molecular biology, 5(1), 1147.
Gordon, G. J., Jensen, R. V., Hsiao, L.-L., Gullans, S. R., Blumenstock, J. E., Ramaswamy, S., . . . Bueno, R. (2002). Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma. Cancer Research, 62(17), 4963-4967.
Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2), 83-90.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
Guyon, I., Gunn, S. R., Ben-Hur, A., & Dror, G. (2004). Result Analysis of the NIPS 2003 Feature Selection Challenge. Paper presented at the NIPS.
Hedenfalk , I., Duggan , D., Chen , Y., Radmacher , M., Bittner , M., Simon , R., . . . Trent , J. (2001). Gene-Expression Profiles in Hereditary Breast Cancer. New England Journal of Medicine, 344(8), 539-548. doi:10.1056/nejm200102223440801
Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., . . . Peterson, C. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature medicine, 7(6), 673-679.
Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59-66. doi:10.1080/00031305.1988.10475524
Pal, M., & Foody, G. M. (2010). Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297-2307.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 6, 2(11), 559-572. doi:10.1080/14786440109462720
Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., & Levine, A. J. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences, 96(12), 6745-6750.
Bellman, R. E. (2015). Adaptive Control Processes: A Guided Tour: Princeton University Press.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, USA.
Boulesteix, A.-L. (2004). PLS Dimension Reduction for Classification with Microarray Data Statistical applications in genetics and molecular biology (Vol. 3, pp. 1).
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018
Cunningham, P. (2008). Dimension Reduction. In M. Cord & P. Cunningham (Eds.), Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval (pp. 91-112). Berlin, Heidelberg: Springer Berlin Heidelberg.
Dai, J. J., Lieu, L., & Rocke, D. (2006). Dimension reduction for classification with gene expression microarray data. Statistical applications in genetics and molecular biology, 5(1), 1147.
Gordon, G. J., Jensen, R. V., Hsiao, L.-L., Gullans, S. R., Blumenstock, J. E., Ramaswamy, S., . . . Bueno, R. (2002). Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma. Cancer Research, 62(17), 4963-4967.
Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2), 83-90.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
Guyon, I., Gunn, S. R., Ben-Hur, A., & Dror, G. (2004). Result Analysis of the NIPS 2003 Feature Selection Challenge. Paper presented at the NIPS.
Hedenfalk , I., Duggan , D., Chen , Y., Radmacher , M., Bittner , M., Simon , R., . . . Trent , J. (2001). Gene-Expression Profiles in Hereditary Breast Cancer. New England Journal of Medicine, 344(8), 539-548. doi:10.1056/nejm200102223440801
Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., . . . Peterson, C. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature medicine, 7(6), 673-679.
Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59-66. doi:10.1080/00031305.1988.10475524
Pal, M., & Foody, G. M. (2010). Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2297-2307.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 6, 2(11), 559-572. doi:10.1080/14786440109462720
Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., . . . Pinkus, G. S. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine, 8(1), 68-74.
Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: A review. Data Classification: Algorithms and Applications, 37.
Tin Kam, H. (1995, 14-16 Aug 1995). Random decision forests. Paper presented at the Proceedings of 3rd International Conference on Document Analysis and Recognition.
Xu, X., & Wang, X. (2005). An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. In X. Li, S. Wang, & Z. Y. Dong (Eds.), Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. Proceedings (pp. 696-703). Berlin, Heidelberg: Springer Berlin Heidelberg.
Yeung, K. Y., & Ruzzo, W. L. (2001). Principal component analysis for clustering gene expression data. Bioinformatics, 17(9), 763-774. doi:10.1093/bioinformatics/17.9.763
林宗勳，Support Vector Machine簡介 |
