博士 / 國立成功大學 / 工業與資訊管理學系 / 103 / Under the increasing pressure of global competition, product life cycles are becoming shorter and shorter. This means that better methods are needed to analyze the limited information obtained at the trial stage in order to derive useful knowledge that can aid mass production. Machine learning algorithms, such as data mining techniques, are widely applied to solve this problem. However, a certain amount of training samples is usually required to determine the validity of the information that is obtained. This study uses only a few data points to estimate the range of data attribute domains with a data diffusion method, in order to derive more useful information. Then, based on practical engineering experience, we generate virtual samples with a noise disturbance method to improve the robustness of the predictions derived from multiple linear regression (MLR). One real dataset obtained from a large TFT-LCD company is examined in the experiment, and the results show that the proposed approach is effective.
| Identifer | oai:union.ndltd.org:TW/103NCKU5041009 |
| Date | January 2015 |
| Creators | Wen-ChihChen, 陳文智 |
| Contributors | Der-Chiang Li, 利德江 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 42 |
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