A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously, for the case of small sample problems with noise, we have done preliminary studies which investigated different learning techniques and different learning functions, separately. In this pa...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/11347/ http://umpir.ump.edu.my/id/eprint/11347/1/A%20Complete%20Investigation%20of%20Using%20Weighted%20Kernel%20Regression%20for%20the%20Case%20of%20Small%20Sample%20Problem%20With%20Noise.pdf |
| Summary: | Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously,
for the case of small sample problems with noise, we have done preliminary studies which investigated different learning
techniques and different learning functions, separately. In this paper, a complete investigation of using WKR for the case of
noisy and small training samples is presented. Analysis and discussion are provided in detail. |
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