隨著資訊發達以及介面易用性的觀念逐漸普及化，人與電腦、資訊裝置、新興消費性電子等的互動逐漸被重視。最被大家所熟悉的就是蘋果電腦(Apple)積極在人機介面的互動創新，因此在iPod、iPhon等產品上，均提出新穎性的互動作法，而SIRI行動助理就是一個典型應用機器學習的人機介面(HCI, Human-Computer Interface)的應用。學習的技術運用在許多的領域，如：人臉辨識、語者辨識、商品推薦系統等等，現有學習方式都是在大量資料中進行運算。本研究提出一個改良的學習排序演算法：除了多階段的作法，兼具(1).考量到成本敏感(Cost-Sensitive)的議題，先分類收斂到小額資料量、並以Boosting演算法可較快速到達排序功能。(2).參考方向一致性(Concordant/Discordant)作法，改善原本二元分類的模式，並計算出排名之間的距離，用以收斂出最適合之前幾筆少數資料量的排序清單。因此本研究研究所提出的方法改善了上述兩個重要地方。就本研究所提出的方法進行實驗，本研究方法在P@n、MAP以及NDCG三項指標皆略優於其它典型方法。本論文也依據所提出多階段適應學習架構，研究成果分別於三項不同領域進行實驗。
首先本研究應用多階段適應學習架構於手勢辨識。第二部份本研究將應用在語者辨識，以語音訊號轉換成語音特徵值後的原生資料進行比對辨識，而本研究中並非專注於數位訊號處理(DSP, Digital Signal Processing)技術的改善。最後，本研究以物聯網(IoT, Internet of Thing)架構的連結器業者為例，提出一個平衡生產線廠區生產力的架構雛型，並且以網頁作人機介面作為上下游業者拉式溝通的模式，並用本方法實驗以安全庫存為基準進行管理訂單派送。

As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in HCI (Human-Computer Interface) which has been used on many products such as iPad and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning Human-Computer Interface.
Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data. In this study, in addition to ranking data through multiple stages, algorithm significantly improves the existing algorithms in two ways. Firstly, it considers the cost-sensitive issue in the ranking algorithm. It classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the concordant and discordant. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. We have also proved in applications in three different areas.
The proposed method was applied to three areas. The experimental results of hand gesture recognition reveal that the efficiency of system execution turns out to be satisfying and the suggested method is desired for application in hand gesture recognition. As for the outcome of average accuracy rate of gesture recognition is more than 98%, a rate of satisfactory. We do not deal the technology improvement with the DSP (Digital Signal Processing). We only process the voice signal converted to the native voice eigenvalue which used to voice recognize. The experiments of the speech recognition show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 96% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person. Finally, we use the Web as the Human-Computer Interface to implement and manage the orders delivered by proposed method. The proposed method in the VMI (Vendor Managed Inventory) system framework achieves an order fulfillment rate 99%, up from the previous 94.75%, or an increase of 4.25% in our experiment result on connector industry. The system is also expected to improve the production efficiency and global competitiveness of the said connector maker.

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