Multi-output Kernel Regression for Correlated Multiple Outputs
4
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (16)
- Tsoumakas G, "Multi-label classification: an overview", IJDWM, 3, pp.1~13 (2007)
- Suykens JAK, "Least square support vector machine classifier", Neural Process Lett, 9, pp.293~300 (1999)
- Suykens JAK, "Optimal control by least squares support vector machines", Neural Netw, 14, pp.23~35 (2001)
- Vapnik VN, "The nature of statistical learning theory" (1995), Springer
- Abdi H, "Encyclopedia for research methods for the social sciences", Sage. chapter Partial least squares (PLS) regression, 792~795 (2013)
- Rosipal R, "Kernel partial least squares regression in reproducing kernel Hilbert space", J Mach Learn Res, 2, pp.97~123 (2001)
- Tuia D, "Multioutput support vector regression for remote sensing bio"physical parameter estimation", IEEE Geosci Remote S, 8, pp.804~808 (2011)
- Liu G, "Multi-output regression on the output manifold", Pattern Recogn, 42, pp.2737~2743 (2009)
- Xu S, "Multi-output least-squares support vector regression machines", Pattern Recogn, 34, pp.1078~1084 (2013)
- Bakker B, "Task clustering and gating for bayesian multitask learning", J Machine Learn Res, 4, pp.83~99 (2003)
- Borchani H, "A survey on multi-output regression", Wires Data Min Knowl, 5, pp.216~233 (2015)
- Xu D, "A Survey on Multi-output Learning", arXiv 2019;1901.00248, ~ (2019)
- Mercer J, "Functions of positive and negative type and their connection with theory of integral equations", Philos T R Soc A, 209, pp.415~446 (1909)
- Kimeldorf G, "Some results on Tchebychean spline functions", J Math Anal Appl, 33, pp.82~95 (1971)
- Wahba G, "Spline models for observational data", CMMS-NSF Regional Conference Series in Applied Mathematics 59. (1990), SIAM
- 양준혁, "Forecasting the KOSPI 200 Stock Index Based on LSTM Autoencoder", Quantitative Bio-Science, 39(2), pp.101~109 (2020), 자연과학연구소[DOI]