Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

Table 1

Analytical results for UA dataset.

Model	MSEP		MSECV		Kernel parameter	Parameter 1^a	Parameter 2^b
IMSVR	1523.42	0.9831	0.0535	0.8433	64	256	0.003906
RSVR	1528.66	0.9830	0.0526	0.8456	64	16	0.003906
KSVR	1530.09	0.9829	0.0526	0.8455	64	1024	0.003906
SLKPLS	1880.18	0.9786	0.0410	0.8804	0.0055243	28	/
GSVR	2021.93	0.9765	0.0448	0.8691	0.005524	512	0.003906
GKPLS	2347.11	0.9740	0.0430	0.8740	0.0039063	25	/
SLSVR	2359.86	0.9731	0.0427	0.8749	0.003906	11.3137	0.003906
IMKPLS	2365.22	0.9721	0.0495	0.8560	32	25	/
RKPLS	2519.68	0.9692	0.0481	0.8691	32	23	/
KKPLS	2613.13	0.9693	0.0481	0.8589	32	23	/
EKPLS	2860.96	0.9692	0.0672	0.8023	0.0625	2	/
ESVR	2971.75	0.9660	0.0597	0.8254	0.003906	90.5097	0.003906
PSVR	5518.49	0.9410	0.0365	0.8935	1	32	0.031250
LinearSVR	5519.22	0.9410	0.0365	0.8935	/	32	0.031250
PKPLS	8554.57	0.9062	0.0393	0.8852	1	10	/
PLS	8554.57	0.9062	0.0393	0.8852	/	10	/

MSEP: mean squared error of prediction; MSECV: mean squared error of cross-validation. : prediction correlation coefficient; : cross-validation correlation coefficient; ^apenalty parameter () for SVR; number of latent variables () for KPLS; ^bnonsensitive loss () for SVR.