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之樣本數很小的資訊，來縮短生產週期、建立穩定的知識模式，於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。因此本研究以盒鬚圖為基礎，先推估資料各屬性的可能值域範圍，再使用啟發式的機制產生虛擬值，以系統性的方法產生更多虛擬樣本，藉以提昇類神經網路之學習效果。本研究以兩組業界資料進行實驗，比較加入虛擬樣本前、後的預測準確率。實驗結果顯示，本研究所產生的虛擬樣本確實能有效改善倒傳遞類神經網路的學習效果。

Product life cycles are becoming shorter and shorter owing to the increasing pressure of global competition. However, companies may dominate their target market if they can shorten their production cycle time by using the knowledge obtained in the pilot runs, where the sample sizes are usually very small, to accelerate their new products coming to market. Machine learning algorithms are widely applied to this task, but the number of training samples is always a key factor in determining their knowledge acquisition capability. Therefore, this study employs the box-and-whisker plots to estimate the domain bounds of a group of observations for sample generation to help the learning tasks. In the experiments, two practical datasets acquired from production pilot runs are examined for the proposed procedure, and the results show that the performance of back-propagation neural networks can be improved significantly.

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