Model Selection Based on the Modulus of Continuity

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32.521.510.50AIC BIC SEB MC(a)32.521.510.50AIC BIC SEB MC(b)32.521.510.50AIC BIC SEB MC(c)32.521.510.50AIC BIC SEB MC(d)Fig. 8. The box-plots of risk ratios r R for the regression of combined function: (a), (b), (c), and (d) represent thebox-plots of r R with σ = 0, 0.025, 0.05, and 0.1 respectively.22
21.510.50AIC BIC SEB MC(a)21.510.50AIC BIC SEB MC(b)21.510.50AIC BIC SEB MC(c)21.510.50AIC BIC SEB MC(d)Fig. 9. The box-plots of node ratios r n for the regression of combined function: (a), (b), (c), and (d) represent thebox-plots of r n with σ = 0, 0.025, 0.05, and 0.1 respectively.23
Page 2: the trade-off between the under-fit
Page 10 and 11: Therefore, the upper bound of risk
Page 12 and 13: similar complexity while the fourth
Page 14 and 15: To see the risk prediction of the m
Page 16 and 17: 32.521.510.50AIC BIC SEB MC(a)32.52
Page 24 and 25: 0.80.60.40.20 0.025 0.05 0.1(a)0.80
Page 26 and 27: 10.90.8train errortest errorBICSEBA
Page 28 and 29: References1. Akaike, H.: Informatio
Page 30: Now, let us consider the target fun

Model Selection Based on the Modulus of Continuity

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