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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124717
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124717

    Title: 強記軟化及整合演算法:以ReLU激發函數與實數輸入/輸出為例
    The Cramming, Softening and Integrating Learning Algorithm with ReLU Activation Function for Real Number Input / Output Problems
    Authors: 梁惟婷
    Liang, Wei-Ting
    Contributors: 蔡瑞煌
    Tsaih, Rua-Huan
    Liang, Wei-Ting
    Keywords: 強記軟化及整合學習
    CSI learning
    Cramming mechanism
    Softening and integrating mechanism
    Least trimmed squares
    Rectified linear units
    Date: 2019
    Issue Date: 2019-08-07 16:08:17 (UTC+8)
    Abstract: 本研究提出了一種基於序列的學習演算法─強記軟化及整合演算法。本研究所提出之演算法具有以下特色:(1) 實現自適應單隱藏層前饋神經網路(adaptive single-hidden layer feed-forward neural networks) (2) 利用最小裁減平方(least trimmed squares)原則決定訓練樣本的輸入序列 (3) 使用線性整流函數(rectified linear units)作為隱藏節點之激發函數 (4) 演算法能在精確學習所有訓練資料之下,同時緩解過度擬合的傾向。本研究對中華航空公司的實際數據進行實驗,以探索(1) 本研究所提出之強記軟化及整合演算法是否能模仿人類的學習方式(2) 本研究所提出之強記軟化及整合演算法是否能不僅精確學習所有的訓練資料,還能透過正規化項、軟化及整合機制來緩解過度擬合的傾向。
    This study proposes the cramming, softening and integrating (CSI) algorithm, a sequentially-learning-based algorithm. The proposed CSI learning algorithm has the following features: (1) the implementation through the adaptive single-hidden layer feed-forward neural networks, (2) the usage of least trimmed squares principle for determining the sequence of learning samples, (3) the usage of rectified linear units activation function, (4) the practice of precisely learning all training data, while alleviating the overfitting pain. An experiment with real data from China Airlines has been conducted to explore (1) whether the proposed CSI algorithm can imitate the way of learning in human beings, as it claims, and (2) whether the proposed CSI algorithm can not only precisely learn all of the training data, but also alleviate the overfitting pain through the regularization term, softening and integrating mechanism.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356035
    Data Type: thesis
    DOI: 10.6814/NCCU201900576
    Appears in Collections:[資訊管理學系] 學位論文

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