政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131496

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131496

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政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/131496

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題名:	自適型單層前饋式類神經網路的裁剪機制與主成分分析 The Pruning Mechanism of Adaptive Single-hidden Layer Neural Networks and Principal Component Analysis
作者:	孫紹傑 Sun, Shao-Chieh
貢獻者:	蔡瑞煌 Tsaih, Rua-Huan 孫紹傑 Sun, Shao-Chieh
關鍵詞:	主成分分析強記、軟化、整合學習演算法人工類神經網路隱藏節點修剪 Principal Component Analysis Cramming, Softening, and Integrating learning algorithm Artificial Neural Network Hidden Node Pruning
日期:	2020
上傳時間:	2020-09-02 11:46:46 (UTC+8)
摘要:	在機器學習領域中的人工類神經網絡（ANN）之架構中，為了解決神經網路學習演算法中過度擬合（overfitting）問題，截至目前尚未有任何系統化的機制可以來幫助我們有效的判別可丟棄的非相關隱藏節點(Irrelevant Hidden Nodes) 。為了解決上述挑戰，我們著重在建立一種系統化結合 PCA (主成分分析) 所提出的 PD（修剪檢測機制）機制，來可靠且有效的決斷出潛在非相關隱藏節點(Potential Irrelevant Hidden Nodes)。本研究所提出的ASLFNPD 運作機制具有以下特點：（1）採用單層隱藏層的神經網（ASLFN）和 ReLU 激活函數;（2）採用PCA 機制幫助辦別潛在非相關隱藏節點(potential irrelevant hidden nodes)。我們進行了實驗並記錄PCA 運作時所產生的 omega 參數數值以及相關資訊，用以驗證所提出的機制具有有效性和效率性。 In order to solve the overfitting problem in the neural network learning issue, there is no systematic mechanism to help us effectively identify Irrelevant Hidden Nodes. To address the above challenges, we focus on establishing a systematic PCA (Principal Component Analysis), PD (Pruning Detection) mechanism to reliably and effectively determine the potential irrelevant hidden nodes. The proposed mechanism ASLFNPD has the following characteristics: (1) applicable to the adaptive single-hidden layer feed-forward neural networks (ASLFN) with the ReLU activation function on all hidden nodes. (2) Use the PCA mechanism to help identify potential irrelevant hidden nodes. We conducted experiments and recorded the omega values generated by PCA and relevant information to verify the effectiveness and efficiency of the proposed mechanism.
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S., Rumelhart, D. E., & Huberman, B. A. (1991). Generalization by weight-elimination with application to forecasting. In Advances in neural information processing systems (pp. 875-882). [21] Krogh, A., & Hertz, J. A. (1992). A simple weight decay can improve generalization. In Advances in neural information processing systems (pp. 950-957). [22] LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimal brain damage. In Advances in neural information processing systems (pp. 598-605). [23] Srivastava, N. (2013). Improving neural networks with dropout. University of Toronto, 182(566), 7. [24] Jackson, J. E. (2005). A user`s guide to principal components (Vol. 587). John Wiley & Sons. (pp. 1-3) [25] Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417. [26] Fisher, R. A., & Mackenzie, W. A. (1923). Studies in crop variation. II. The manurial response of different potato varieties. 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描述:	碩士國立政治大學資訊管理學系 107356022
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0107356022
資料類型:	thesis
DOI:	10.6814/NCCU202001062
顯示於類別:	[資訊管理學系] 學位論文

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