藉由大量學習資料，機器學習演算法利用統計理論在資料中分析過往經驗，並增進自動分類器及預測器的準確率。在這篇論文中，我們提出了一個新的統計型學習方法，這個以AdaBoost演算法為基礎的二元分類方法，利用成對特徵學習來增進分類準確性．此學習方法已被應用來解決四個不同的問題。
成對特徵學習的目的是在機器學習的過程中，以不增加過多額外運算的前提，有效提高辨識能力並保留學習資料的資訊。我們提出的學習方法主要包含四個主要的步驟：特徵域扭曲、成對特徵共同表述、類ID3樹狀平面量化、機率型弱分類器。我們提出的演算法利用上述步驟更新學習資料權重，並得到更快速、更準確的AdaBoost二元分類器。
我們提出的方法已經被應用於四個完全不同的分類問題來展現其鑑別率。第一個實驗基於影像內容來完成影像搜尋系統（CBIR），這個利用人機互動及回饋式學習的應用，可被視為在不充足學習資料前提下的二元分類問題。接著，我們利用這篇論文提出的成對特徵學習，開發出一套可直接在小波壓縮域上進行人臉偵測的新演算法。第三個應用是在核磁共振影像（MRI）上，進行脊椎骨的自動偵測及圖素準確度的影像切割，我們在此研究中，整合所提出的機器學習技術、曲線擬合強固偵測、步進式normalized-cut影像切割，進而在測試核磁共振影像中達到近98%的高偵測率及高切割準確度。最後，我們利用此二元分類演算法來增強投資學中的反轉交易策略，整合機器學習及財務金融兩個研究領域的知識，使用雙分類器模型找尋時間序列中的特定樣型，此方法有效被驗証在S&P100的成份股投資中。總結說來，這些應用皆顯示我們提出的成對特徵學習演算法，可大幅增進分類準確率。

Statistical machine learning provides a statistical framework to improve the performance of classifiers or predictors learned from large amounts of training data collected from past experiences. In this thesis, we propose a novel AdaBoost-based algorithm based on paired feature learning to improve the performance of classifiers for several different applications.
The proposed learning system contains four major improvements with the goal to achieve higher discrimination and more information preservation in the paired feature learning for the AdaBoost classifiers. These improvements include feature space warping, joint feature representation, ID3-like plane quantization and weak probabilistic classifiers. The proposed algorithm updates the sample weights via the proposed processes and achieves a more efficient and accurate AdaBoost classifier.
The proposed classification method has been applied to four different applications to demonstrate its discrimination power. First, the proposed algorithm is applied to content-based image retrieval (CBIR) with relevance feedback, which can be formulated as a classification problem with a small number of training samples. Our experiments show superior performance of the proposed system compared to some previous methods. Secondly, we develop a new face detection algorithm that works directly on the wavelet compressed domain based on the same paired feature learning framework. The third application is the vertebra detection and segmentation from a spinal magnetic resonance (MR) image. The modified AdaBoost classifier is used in conjunction with a robust spinal curve fitting technique and iterative normalized cut segmentation, and the proposed system can achieve nearly 98% vertebra detection rate and high segmentation accuracy on a variety of testing spinal MR images. Finally, we apply the same paired feature learning technique to improve the contrarian trading strategy in computational finance. A dual classifiers model is adopted to discover the time-series patterns constructed by the integrated knowledge of finance and machine learning techniques. Our experiments on S&P 100 index component stocks show dramatic improvement by using the proposed algorithm. In summary, all these experiments demonstrate the improvement in accuracy by using the proposed paired feature learning in an AdaBoost classification framework.

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