Universiti Teknologi Malaysia Institutional Repository

Recipe generation from small samples: incorporating an improved weighted kernel regression with correlation factor

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

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Official URL: http://dx.doi.org/10.1109/ICMSAO.2011.5775473

Abstract

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.

Item Type:Book Section
Uncontrolled Keywords:recipe generation, predictive modeling, weighted kernel regression, small samples, correlation factor
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:29576
Deposited By:INVALID USER
Deposited On:19 Mar 2013 08:05
Last Modified:27 Jul 2017 04:53

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