An improved kernel regression method based on Taylor expansion
文献类型：EI期刊论文
| 作者 | Zhang Jiang-She ; Huang Xing-Fang ; Zhou Cheng-Hu |
| 发表日期 | 2007 |
| 关键词 | Image enhancement Parameter estimation Problem solving Regression analysis |
| 英文摘要 | Many regression functions obtained by nonparametric regression method often appear inconsonance between smoothness and fitness. This phenomenon is extremely outstanding near the vertex regions. How to improve the fitness and smoothness simultaneously becomes an important problem in the nonparametric regression field. In this paper, an improved kernel regression is proposed by introducing second derivative estimation into kernel regression function based on Taylor expansion theorem. Experimental results on regression problems show that this new method is feasible and enables us to get regression function that is both smooth and well-fitting. The application of the method to grey image enhancement indicates that this approach is fruitful to the enhancement of weak information in the images. © 2007 Elsevier Inc. All rights reserved. |
| 出处 | Applied Mathematics and Computation
 |
| 卷 | 193期:2页:419-429 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24391]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Zhang Jiang-She,Huang Xing-Fang,Zhou Cheng-Hu. An improved kernel regression method based on Taylor expansion. 2007. |
入库方式： OAI收割
来源：地理科学与资源研究所
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