小樣本學習問題，常發生於系統建置初期資料取得困難或以及取得成本過高卻必須從事學習之情況，因此如何從中學習更多有意義的資訊，於近年已成為研究的課題。在過往學習方法中，虛擬樣本產生法已驗證為有效的方法之一，然其中如整體趨勢(mega-trend-diffusion, MTD)技術缺乏對資料進行事前分析，其於母體推論時雖考量其偏態但欠缺母體存在多峰之可能。因此本研究在MTD產生虛擬樣本前，先藉由模糊分群法Fuzzy C-means以及模糊側影係數學習小樣本分布狀況之事前資訊。在實驗階段，本研究以兩筆從電子製造業取得之真實案例進行驗證，結果顯示當訓練樣本加入本研究方法所產生之虛擬樣本後，確實較加入MTD所產生者在倒傳遞類神經網路上有更佳之預測準確度，亦表示此種基於事前資訊所建構的樣本分配，確能改善產生之虛擬樣本品質。

Small dataset learning problems usually occurs when systems are constructed at the beginning stage, where samples are hard to obtain or the collecting cost is extremely high, and there still exists the necessary to achieve the learning tasks. Therefore, learning more meaningful information from small datasets has become an important issue in recent years. Creating more virtual samples to increase the data sizes has been demonstrated to be an effective approach when leaning small datasets. However, the mega-trend-diffusion technique (MTD) lacks the prior analysis of data. Although MTD takes the skewness into account when inferring the population distributions, the possibility that the populations have multiple peaks is absent. Accordingly, this research obtains the prior distribution information of small datasets by using the fuzzy C-means and the fuzzy silhouette coefficients before the MTD begins to generate samples. In the experiments, two real cases taken from the electronics industry are examined. The results show that when the training sets contain the samples created by the proposed method, the predictions of the Back-Propagation Neural Networks (BPN) are more accurate than those when the training sets contain the samples created by MTD. This further demonstrates the quality of virtual samples is thus improved when those are generated from the distributions which are reconstructed based on the prior information

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