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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.16 No.1
- 발행연도
- 2016.3
- 수록면
- 44 - 51 (8page)
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초록· 키워드
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Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users’ sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.
목차
Abstract
1. Introduction
2. Preliminaries
3. Applications
4. Conclusion
References
참고문헌 (35)
참고문헌 신청
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N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
B. Sch˝olkopf and A. J. Smola, Learning with Kernels, MIT Press, 2002.
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Schlkopf, “An introduction to kernel-based learning algo- rithms,” IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 181-201, 2001.
J. T. Kwok, “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1162-1173, 2000.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
B. Sch˝olkopf and A. J. Smola, Learning with Kernels, MIT Press, 2002.
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Schlkopf, “An introduction to kernel-based learning algo- rithms,” IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 181-201, 2001.
J. T. Kwok, “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1162-1173, 2000.
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UCI(KEPA) : I410-ECN-0101-2016-028-002742315