在越來越嚴苛的全球化競爭壓力下，產品生命週期變得越來越短。因此，如何運用試產階段所得到的稀少資訊，於新產品開發階段建立穩定的知識模式，以縮短研發週期、快速導入量產，來縮短產品上市時程，在近年來已是重要的議題。機器學習演算法被廣泛應用於這樣的課題，然而訓練樣本的數量始終為決定其資訊獲取能力的關鍵因素。本研究中，僅以少數幾筆資料透過整體趨勢擴散技術進行屬性值域範圍之推估，然後根據實際的工程經驗，於模型中加入轉換函數及雜訊因子來穩健從多元迴歸中篩選出來的有效虛擬樣本。本論文以液晶面板產業的試產資料進行研究，結果證明所提出的方法是有效的。

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.

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