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Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data

Citations

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Cited by:

  1. Liu, Yufeng & Helen Zhang, Hao & Park, Cheolwoo & Ahn, Jeongyoun, 2007. "Support vector machines with adaptive Lq penalty," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6380-6394, August.
  2. Zhilan Lou & Jun Shao & Menggang Yu, 2018. "Optimal treatment assignment to maximize expected outcome with multiple treatments," Biometrics, The International Biometric Society, vol. 74(2), pages 506-516, June.
  3. Abramovich, Felix & Pensky, Marianna, 2019. "Classification with many classes: Challenges and pluses," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
  4. Mounia Hendel & Fethi Meghnefi & Mohamed El Amine Senoussaoui & Issouf Fofana & Mostefa Brahami, 2023. "Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic," Sustainability, MDPI, vol. 15(21), pages 1-23, October.
  5. Engin Tas & Ayca Hatice Atli, 2024. "Stock Price Ranking by Learning Pairwise Preferences," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 513-528, February.
  6. Sarra Houidi & Dominique Fourer & François Auger & Houda Ben Attia Sethom & Laurence Miègeville, 2021. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning," Energies, MDPI, vol. 14(9), pages 1-28, May.
  7. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
  8. Keiji Tatsumi & Tetsuzo Tanino, 2014. "Support vector machines maximizing geometric margins for multi-class classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 815-840, October.
  9. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  10. Park, Changyi & Koo, Ja-Yong & Kim, Peter T. & Lee, Jae Won, 2008. "Stepwise feature selection using generalized logistic loss," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3709-3718, March.
  11. Xingye Qiao & Yufeng Liu, 2009. "Adaptive Weighted Learning for Unbalanced Multicategory Classification," Biometrics, The International Biometric Society, vol. 65(1), pages 159-168, March.
  12. Hoai An Le Thi & Manh Cuong Nguyen, 2017. "DCA based algorithms for feature selection in multi-class support vector machine," Annals of Operations Research, Springer, vol. 249(1), pages 273-300, February.
  13. Víctor Blanco & Alberto Japón & Justo Puerto, 2020. "Optimal arrangements of hyperplanes for SVM-based multiclass classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 175-199, March.
  14. Fan, Yiwei & Zhao, Junlong, 2022. "Safe sample screening rules for multicategory angle-based support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  15. Park, Beomjin & Park, Changyi, 2021. "Kernel variable selection for multicategory support vector machines," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
  16. Yoonkyung Lee, 2014. "Comments on: Support vector machines maximizing geometric margins for multi-class classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 852-855, October.
  17. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
  18. Gardner-Lubbe, Sugnet, 2016. "A triplot for multiclass classification visualisation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 20-32.
  19. Guangrui Tang & Neng Fan, 2022. "A Survey of Solution Path Algorithms for Regression and Classification Models," Annals of Data Science, Springer, vol. 9(4), pages 749-789, August.
  20. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
  21. Qi Wang & Yue Ma & Kun Zhao & Yingjie Tian, 2022. "A Comprehensive Survey of Loss Functions in Machine Learning," Annals of Data Science, Springer, vol. 9(2), pages 187-212, April.
  22. Fu, Sheng & Zhang, Sanguo & Liu, Yufeng, 2018. "Adaptively weighted large-margin angle-based classifiers," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 282-299.
  23. Maximilian Alber & Julian Zimmert & Urun Dogan & Marius Kloft, 2017. "Distributed optimization of multi-class SVMs," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-18, June.
  24. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  25. Ozcan, Sercan & Suloglu, Metin & Sakar, C. Okan & Chatufale, Sushant, 2021. "Social media mining for ideation: Identification of sustainable solutions and opinions," Technovation, Elsevier, vol. 107(C).
  26. Aijun Yang & Yunxian Li & Niansheng Tang & Jinguan Lin, 2015. "Bayesian variable selection in multinomial probit model for classifying high-dimensional data," Computational Statistics, Springer, vol. 30(2), pages 399-418, June.
  27. Hossein Baloochian & Hamid Reza Ghaffary, 2019. "Multiclass Classification Based on Multi-criteria Decision-making," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 140-151, April.
  28. Safo, Sandra E. & Ahn, Jeongyoun, 2016. "General sparse multi-class linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 81-90.
  29. Ali Anaissi & Madhu Goyal & Daniel R Catchpoole & Ali Braytee & Paul J Kennedy, 2016. "Ensemble Feature Learning of Genomic Data Using Support Vector Machine," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
  30. Song Huang & Tiejun Tong & Hongyu Zhao, 2010. "Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification," Biometrics, The International Biometric Society, vol. 66(4), pages 1096-1106, December.
  31. Chakraborty, Sounak & Guo, Ruixin, 2011. "A Bayesian hybrid Huberized support vector machine and its applications in high-dimensional medical data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1342-1356, March.
  32. Crystal T. Nguyen & Daniel J. Luckett & Anna R. Kahkoska & Grace E. Shearrer & Donna Spruijt‐Metz & Jaimie N. Davis & Michael R. Kosorok, 2020. "Estimating individualized treatment regimes from crossover designs," Biometrics, The International Biometric Society, vol. 76(3), pages 778-788, September.
  33. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
  34. van den Burg, G.J.J. & Groenen, P.J.F., 2014. "GenSVM: A Generalized Multiclass Support Vector Machine," Econometric Institute Research Papers EI 2014-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  35. Peter Hall & D. M. Titterington & Jing‐Hao Xue, 2009. "Tilting methods for assessing the influence of components in a classifier," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 783-803, September.
  36. Yann Guermeur, 2014. "Comments on: Support Vector Machines Maximizing Geometric Margins for Multi-class Classification," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 844-851, October.
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