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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/124709
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124709

    Title: 以序列型學習演算法預測牛市與熊市之轉折點
    The sequentially-learning-based algorithm and the pre-diction of the turning points of bull and bear markets
    Authors: 張瑄芸
    Chang, Hsuan-Yun
    Contributors: 蔡瑞煌

    Tsaih, Rua-Huan
    Lu, Chinh-Chih

    Chang, Hsuan-Yun
    Keywords: 概念飄移
    concept drifting
    outlier detection
    artificial neural network
    moving window
    cramming mechanism
    softening mechanism
    integrating mechanism
    Date: 2019
    Issue Date: 2019-08-07 16:06:39 (UTC+8)
    Abstract: 在機器學習領域中,對於人工神經網絡(ANN)的架構中,輸入值是實數,輸出值是二元的問題是有挑戰性的,尚未有任何神經網路學習演算法可以解決過度擬合(overfitting)問題,同時可以完美地學習所有訓練數據;另外,在概念飄移環境中要如何去處理離群值的偵測也變得重要,現今的很多資料多為動態性且具概念漂移的特性。
    In the field of machine learning, there is a challenge to the Artificial Neural Networks (ANN) application whose input values are real numbers and the output values are binary. Whether any of ANN learning algorithms can solve the overfitting problem, while it can perfectly learn all of training data. Besides, the problem of outlier detection in the concept environment is becoming an issue. The nature data now has the dynamic and unstable property in the concept drifting environment.
    To address the aforementioned challenge, this study proposes the DSM (Decision Support Mechanism) and CSI (Cramming, Softening, and Integrating) learning algorithm. DSM apply the moving window mechanism, and it can not only identify the potential turning point detection in the bull/ bear market but also assist the decision maker to double check merely all of turning point candidates. The proposed CSI learning algorithm has the follow-ing features: (1) the adoption of adaptive single-hidden layer feed-forward neural network (ASLFN) and ReLU activation function, (2) the usage of least trimmed squares (LTS) prin-ciple to speed up the training time, (3) the practice to precisely learn all training data, and (4) the implementations of the regularization term, the softening and integrating mechanism to alleviate the obtained model from the overfitting tendency. We conduct an experiment of detecting the turning points of bull/bear markets to validate the effectiveness and efficiency of the proposed algorithm in the addressing challenge.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356017
    Data Type: thesis
    DOI: 10.6814/NCCU201900325
    Appears in Collections:[Department of MIS] Theses

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