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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.11
- 수록면
- 83 - 88 (6page)
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초록· 키워드
오류제보하기
The goal of multi-output regression is to predict multiple realvalued output variables. Research in this area is lacking compared to multi-output classification . In this approach multi-output kernel regression based on kernel regression in which different outputs are assumed to be correlated. We also introduce a modelselection method employs generalized crossvalidation function for choosing optimal values of hyperparameters. Numerical results from synthetic and real datasets are then obtained to illustrate that the proposed outperforms the other methods on multi-output regression problems.
목차
ABSTRACT
1. Introduction
2. Multi-output LS-SVR
3. Multi-output Kernel Regression
4. Numerical Studies
5. Conclusions
References
참고문헌 (16)
참고문헌 신청
Tsoumakas G / 2007 / Multi-label classification: an overview / IJDWM 3:1~13
Suykens JAK / 1999 / Least square support vector machine classifier / Neural Process Lett 9:293~300
Suykens JAK / 2001 / Optimal control by least squares support vector machines / Neural Netw 14:23~35
Vapnik VN / 1995 / The nature of statistical learning theory / Springer
Abdi H / 2013 / Encyclopedia for research methods for the social sciences / Sage. chapter Partial least squares (PLS) regression :792~795
Suykens JAK / 1999 / Least square support vector machine classifier / Neural Process Lett 9:293~300
Suykens JAK / 2001 / Optimal control by least squares support vector machines / Neural Netw 14:23~35
Vapnik VN / 1995 / The nature of statistical learning theory / Springer
Abdi H / 2013 / Encyclopedia for research methods for the social sciences / Sage. chapter Partial least squares (PLS) regression :792~795
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