Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis
Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the inpu...
Saved in:
| Main Authors: | Xiaoyan Ma, Yanbin Zhang, Hui Cao, Shiliang Zhang, Yan Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2018/2689750 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Lithological classification using SDGSAT-1 TIS data and three-dimensional spectral feature space model
by: Qunjia Zhang, et al.
Published: (2025-08-01) -
Convergence Theorems for Accretive Operators with Nonlinear Mappings in Banach Spaces
by: Yan-Lai Song, et al.
Published: (2014-01-01) -
Common Fixed Point Theorems for Nonlinear Contractive Mappings in Dislocated Metric Spaces
by: Yijie Ren, et al.
Published: (2013-01-01) -
Evolution of the Three Spectral Components in the Prompt Emission of GRB 240825A
by: Chen-Wei Wang, et al.
Published: (2025-01-01) -
Tripled Fixed Points of Multivalued Nonlinear Contraction Mappings in Partially Ordered Metric Spaces
by: Mujahid Abbas, et al.
Published: (2011-01-01)