建議

第五章 結論與建議

本章節將針對本研究方法做個結論並提出建議供有意想針對本研究繼續發 展的研究者參考。

5.1 結論

本研究利用了成對約束法及樣本間有群聚性的性質先用分群的方式先將所 有樣本分群，在少樣本情況下，還是可以得到未知類別樣本間的一些可用資訊來 進行訓練，所以在低維度下還是擁有不錯的正確率，再加上樣本多的時候也能使 辨識能力更加穩定，所以更加確信利用結合分群法與成對約束法在鄰接矩陣的設 計上可以獲致更佳正確率。

5.2 建議

首先在分群和決定邊界點的方法上，尚有其他方法，例如模糊cc-均值分群法 (fuzzy cc-means clustering)[43]和後驗機率(posterior probability)[5], [17], [18]可以 分別用來分群和判別是否為邊界點的方法。最後，本研究在目前主要針對在小樣 本資料集有不錯的辨識效果，希望往後可以進一步針對大樣本資料集，例如：人 臉圖、高光譜圖像、指紋辨識圖，期待有意研究的研究人員可以將本研究方法加 上前面建議加以修正改良運用在這些圖像上。

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