Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor
The cost of the experimental setup during the assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under-fill process consist of only a few samples. As a resu...
| Main Authors: | , , , , , |
|---|---|
| Format: | Book Section |
| Published: |
Springer-Verlag
2011
|
| Subjects: |
| _version_ | 1796856580462870528 |
|---|---|
| author | Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Lee, Wen Jau Ong, Soon-Chuan Pavlovich, Vladimir |
| author_facet | Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Lee, Wen Jau Ong, Soon-Chuan Pavlovich, Vladimir |
| author_sort | Shapiai, Mohd. Ibrahim |
| collection | ePrints |
| description | The cost of the experimental setup during the assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under-fill process consist of only a few samples. As a result, existing machine learning algorithms cannot be applied in this setting. To solve this problem, predictive modeling algorithm called Weighted Kernel Regression with correlation factor (WKRCF), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. The correlation factor reflected the important features by changing the bandwidth of the kernel as a function of the output. Even though only four samples are used during the training stage, the WKRCF provides an accurate prediction as compared with other techniques including the NWKR and the artificial neural networks with back-propagation algorithm (ANNBP). Thus, the proposed approach is beneficial for recipe generation in an assembly process development. |
| first_indexed | 2024-03-05T18:45:10Z |
| format | Book Section |
| id | utm.eprints-29576 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:45:10Z |
| publishDate | 2011 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | utm.eprints-295762017-07-27T04:53:13Z http://eprints.utm.my/29576/ Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Lee, Wen Jau Ong, Soon-Chuan Pavlovich, Vladimir TK Electrical engineering. Electronics Nuclear engineering The cost of the experimental setup during the assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under-fill process consist of only a few samples. As a result, existing machine learning algorithms cannot be applied in this setting. To solve this problem, predictive modeling algorithm called Weighted Kernel Regression with correlation factor (WKRCF), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. The correlation factor reflected the important features by changing the bandwidth of the kernel as a function of the output. Even though only four samples are used during the training stage, the WKRCF provides an accurate prediction as compared with other techniques including the NWKR and the artificial neural networks with back-propagation algorithm (ANNBP). Thus, the proposed approach is beneficial for recipe generation in an assembly process development. Springer-Verlag 2011 Book Section PeerReviewed Shapiai, Mohd. Ibrahim and Ibrahim, Zuwairie and Khalid, Marzuki and Lee, Wen Jau and Ong, Soon-Chuan and Pavlovich, Vladimir (2011) Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor. In: Communications in Computer and Information Science. Springer-Verlag, Kuantan, Pahang, pp. 144-154. ISBN 978-3-64222169-9 http://dx.doi.org/10.1109/ICMSAO.2011.5775473 10.1007/978-3-642-22170-5_13 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Shapiai, Mohd. Ibrahim Ibrahim, Zuwairie Khalid, Marzuki Lee, Wen Jau Ong, Soon-Chuan Pavlovich, Vladimir Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title | Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title_full | Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title_fullStr | Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title_full_unstemmed | Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title_short | Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor |
| title_sort | recipe generation from small samples incorporating an improved weighted kernel regression with correlation factor |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT shapiaimohdibrahim recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor AT ibrahimzuwairie recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor AT khalidmarzuki recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor AT leewenjau recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor AT ongsoonchuan recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor AT pavlovichvladimir recipegenerationfromsmallsamplesincorporatinganimprovedweightedkernelregressionwithcorrelationfactor |