Risk management approaches in data mining
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[1] Wenjie Hu,et al. Robust support vector machine with bullet hole image classification , 2002 .
[2] Philippe Artzner,et al. Coherent Measures of Risk , 1999 .
[3] R. Horst,et al. Global Optimization: Deterministic Approaches , 1992 .
[4] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[5] G. Pflug,et al. Value-at-Risk in Portfolio Optimization: Properties and Computational Approach ⁄ , 2005 .
[6] R. Rockafellar,et al. Optimization of conditional value-at risk , 2000 .
[7] Stan Uryasev,et al. Value-at-risk support vector machine: stability to outliers , 2013, Journal of Combinatorial Optimization.
[8] Yuanyuan Wang,et al. A rough margin based support vector machine , 2008, Inf. Sci..
[9] Le Thi Hoai An,et al. The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems , 2005, Ann. Oper. Res..
[10] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[11] Helmut Mausser,et al. ALGORITHMS FOR OPTIMIZATION OF VALUE AT-RISK* , 2002 .
[12] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[13] Panos M. Pardalos,et al. Introduction to Global Optimization , 2000, Introduction to Global Optimization.
[14] Xiaoguang Yang,et al. Complexity of Scenario-Based Portfolio Optimization Problem with VaR Objective , 2002, Int. J. Found. Comput. Sci..
[15] Chih-Jen Lin,et al. Training v-Support Vector Regression: Theory and Algorithms , 2002, Neural Computation.
[16] D. Duffie,et al. An Overview of Value at Risk , 1997 .
[17] Philippe Jorion. Value at risk: the new benchmark for controlling market risk , 1996 .
[18] C. Acerbi. Spectral measures of risk: A coherent representation of subjective risk aversion , 2002 .
[19] Theodore B. Trafalis,et al. Robust classification and regression using support vector machines , 2006, Eur. J. Oper. Res..
[20] Akiko Takeda,et al. Interaction between financial risk measures and machine learning methods , 2014, Comput. Manag. Sci..
[21] R. Rockafellar,et al. The fundamental risk quadrangle in risk management, optimization and statistical estimation , 2013 .
[22] Stan Uryasev,et al. Statistical Decision Problems: Selected Concepts and Portfolio Safeguard Case Studies , 2013 .
[23] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[24] Stan Uryasev,et al. Advanced risk measures in estimation and classification , 2012 .
[25] Gunnar Rätsch,et al. An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.
[26] Bernhard Schölkopf,et al. Extension of the nu-SVM range for classification , 2003 .
[27] Akiko Takeda,et al. ν-support vector machine as conditional value-at-risk minimization , 2008, ICML '08.
[28] R. Horst,et al. DC Programming: Overview , 1999 .
[29] Sheng-De Wang,et al. Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.
[30] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[31] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[32] Xuegong Zhang,et al. Using class-center vectors to build support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[33] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[34] Akiko Takeda,et al. A linear classification model based on conditional geometric score , 2004 .
[35] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.