機器學習及其應用

Description

　　本書內容涉及穩健的矩陣回歸模型、初等模型的解析解、面向大規模學習的隨機優化、張量分解、基於遞歸神經網路的圖像描述算法、標記分布學習及其應用，以及軟體缺陷挖掘。 　　楊健教授介紹了幾種全新的穩健回歸模型，包括基於核範數的穩健矩陣回歸，基於推廣冪指數分布的穩健矩陣回歸，基於核-L1範數的聯合矩陣回歸，以及基於樹結構核範數的穩健矩陣回歸。 　　林宙辰教授討論了若干具有解析解的初等模型，以及其在圖像處理領域的諸多成功應用。 　　在大規模數據的機器學習算法中，隨機近似是一種新興的技術手段。張利軍教授介紹了階段混合梯度下降、隨機臨近梯度下降，能夠有效降低學習算法的空間和時間複雜度。 　　張量結構往往用於刻劃數據的多源關係。徐增林教授介紹了非參非線性張量分解，能夠提高分解模型的精確度。 　　圖像描述是近年來有挑戰性的任務。張長水教授介紹了基於注意力的遞歸神經網路編碼器，刻畫了圖像和句子之間的編碼關係。 　　和圖像描述類似，大量的機器學習樣本涉及標記多義性的問題。耿新教授詳細介紹了一種新的機器學習設計法——標記分布學習，為解決標記多義性開闢了一條新途徑。 　　最後，黎銘副教授探討了機器學習技術在軟體缺陷挖掘中的創新應用，以及其中面臨的若干挑戰性問題。

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