政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124717

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124717

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113648/144635 (79%)
造访人次 : 51643920 在线人数 : 538

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/124717

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/124717

题名:	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
作者:	梁惟婷 Liang, Wei-Ting
贡献者:	蔡瑞煌 Tsaih, Rua-Huan 梁惟婷 Liang, Wei-Ting
关键词:	強記軟化及整合學習強記機制軟化及整合機制最小裁減平方正規化線性整流函數 CSI learning Cramming mechanism Softening and integrating mechanism Least trimmed squares Regularization Rectified linear units
日期:	2019
上传时间:	2019-08-07 16:08:17 (UTC+8)
摘要:	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.
參考文獻:	[1] B. Freisleben, and G. Gleichmann, “Controlling airline seat allocations with neural networks,” in System Sciences, Proceeding of the Twenty-Sixth Hawaii International Conference on. IEEE, vol. 4, pp. 635-642, January 1993. [2] C. Lemke, S. Riedel, and B. Gabrys, “Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations,” IEEE Symposium on Computational Intelligence for Financial Engineering Proceedings, pp. 85-91, 2009. [3] C. Meng, M. Sun, J. Yang, M. Qiu, and Y. Gu, “Training Deeper Models by GPU Memory Optimization on TensorFlow,” in Proc. of ML Systems Workshop in NIPS, December, 2017. [4] G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Neural Networks, Proceedings, IEEE International Joint Conference on. IEEE, vol. 2, pp. 985-990, 2004. [5] H. S. Jenatabadi, and N. A. Ismail, “The determination of load factors in the airline industry,” International Review of Business Research Papers, vol. 3, no. 4, pp. 125-133, 2007. [6] K. Littlewood, “Special issue papers: Forecasting and control of passenger bookings,” Journal of Revenue and Pricing Management, vol. 4, no.2, pp. 111-123, 2005. [7] K. T. Wu, and F. C. Lin, “Forecasting airline seat show rates with neural networks,” in Neural Networks, IJCNN`99, International Joint Conference on. IEEE, vol. 6, pp. 3974-3977, July 1999. [8] L. Devriendt, G. Burghouwt, B. Derudder, J. D. Wit, and F. Witlox, “Calculating load factors for the transatlantic airline market using supply and demand data – A note on the identification of gaps in the available airline statistics,” Journal of Air Transport Management, vol. 15, no. 6, pp. 337-343, 2009. [9] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 165-283, 2016. [10] M. Babaeizadeh, I. Frosio, S. Tyree, J. Clemons, and J. Kautz, “GA3C: GPU-based A3C for deep reinforcement learning,” CoRR abs/1611.06256, 2016. [11] M. O. Aomo, D. O. Oima, and M. N. Oginda, “An Empirical Investigation into the Effect of Enhancing Airline Capacity on Load Factor: A Case of Kenya’s Low-Cost Carriers,” American Journal of Industrial and Business Management, vol. 6, no. 6, pp. 717-731, 2016. [12] R. H. Tsaih, and T. C. Cheng, “A resistant learning procedure for coping with outliers,” Annals of Mathematics and Artificial Intelligence, vol. 57, no.2, pp. 161-180, 2009. [13] R. H. Tsaih, B. S. Kuo, T. H. Lin, and C. C. Hsu, “The use of big data analytics to predict the foreign exchange rate based on public media: A machine-learning experiment,” IT Professional, vol. 20, no. 2, pp. 34-41, 2018. [14] R. R. Tsaih, “An explanation of reasoning neural networks,” Mathematical and computer modelling, vol. 28, no. 2, pp. 37-44, 1998. [15] R. R. Tsaih, “The softening learning procedure,” Mathematical and computer modelling, vol. 18, no. 8, pp. 61-64, 1993. [16] S. Belciug, and F. Gorunescu, “Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection,” Journal of biomedical informatics, vol. 83, pp. 159-166, July 2018. [17] S. Y. Huang, J. W. Lin, and R. H. Tsaih, “Outlier detection in the concept drifting environment,” in Neural Networks, International Joint Conference on. IEEE, vol. 6, pp. 31-37, November 2016. [18] S. Y. Huang, R. H. Tsaih, and F. Yu, “Topological pattern discovery and feature extraction for fraudulent financial reporting,” Expert Systems with Applications, vol. 41, no. 9, pp. 4360-4372, 2014. [19] W. Peetawan, “Determination of passenger load factor: the case of Thai Airlines,” unpublished, April 2018. [20] X. S. Sun, E. Brauner, and S. Hormby, “A large-scale neural network for airline forecasting in revenue management,” in Operations Research in the Airline Industry, vol. 9, Springer: Boston, MA, 1998, pp. 46-67. [21] Y. Y. Tesfay, “Modified panel data regression model and its applications to the airline industry: Modeling the load factor of Europe North and Europe Mid Atlantic flights,” Journal of Traffic and Transportation Engineering, English ed., vol. 3, no. 4, pp. 283-295, 2016. [22] B. Xu, N. Wang, T. Chen, and M. Li, “Empirical Evaluation of Rectified Activations in Convolution Network,” arXiv preprint, arXiv: 1505.00853, 2005. [23] V. Nair, and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceeding of the 27th international conference on machine learning, pp. 807-814, 2010.
描述:	碩士國立政治大學資訊管理學系 106356035
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106356035
数据类型:	thesis
DOI:	10.6814/NCCU201900576
显示于类别:	[資訊管理學系] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
603501.pdf	1510Kb	Adobe PDF2	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈