المؤلفون: 紀亞妤, Chi, Ya-Yu

المساهمون: 郁方, Yu, Fang

مصطلحات موضوعية: 動態符號執行測試, 對抗式生成攻擊, 神經網路模型, Concolic Testing, Adversarial attack, Neural Network model

وصف الملف: 4467534 bytes; application/pdf

Relation: [1] Pyct-rq: Constraint-based concolic testing for neural networks. https://github.com/ManticoreDai/PyCT-rq. [2] S. Cha, S. Hong, J. Bak, J. Kim, J. Lee, and H. Oh. Enhancing dynamic symbolic execution by automatically learning search heuristics. IEEE Transactions on Software Engineering, 48(9):3640–3663, 2021. [3] H. Chen, S. M. Lundberg, and S.-I. Lee. Explaining a series of models by propagating shapley values. Nature communications, 13(1):4512, 2022. [4] Y.-F. Chen, W.-L. Tsai, W.-C. Wu, D.-D. Yen, and F. Yu. Pyct: A python concolic tester. In Programming Languages and Systems: 19th Asian Symposium, APLAS 2021, Chicago, IL, USA, October 17–18, 2021, Proceedings 19, pages 38–46. Springer, 2021. [5] S. Fortz, F. Mesnard, E. Payet, G. Perrouin, W. Vanhoof, and G. Vidal. An smt-based concolic testing tool for logic programs. In International Symposium on Functional and Logic Programming, pages 215–219. Springer, 2020. [6] I. Fursov, A. Zaytsev, N. Kluchnikov, A. Kravchenko, and E. Burnaev. Gradientbased adversarial attacks on categorical sequence models via traversing an embedded world. In Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers 9, pages 356–368. Springer, 2021. [7] I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. MIT press, 2016. [8] I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. [9] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6):82–97, 2012. [10] W. Huang, Y. Sun, X. Zhao, J. Sharp, W. Ruan, J. Meng, and X. Huang. Coverageguided testing for recurrent neural networks. IEEE Transactions on Reliability, 71(3):1191–1206, 2021. [11] G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer. Reluplex: An efficient smt solver for verifying deep neural networks. In Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I 30, pages 97–117. Springer, 2017. [12] G. Katz, D. A. Huang, D. Ibeling, K. Julian, C. Lazarus, R. Lim, P. Shah, S. Thakoor, H. Wu, A. Zeljić, et al. The marabou framework for verification and analysis of deep neural networks. In Computer Aided Verification: 31st International Conference, CAV 2019, New York City, NY, USA, July 15-18, 2019, Proceedings, Part I 31, pages 443–452. Springer, 2019. [13] Y. Kim, S. Hong, and M. Kim. Target-driven compositional concolic testing with function summary refinement for effective bug detection. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 16–26, 2019. [14] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012. [15] Z. Li, X. Ma, C. Xu, and C. Cao. Structural coverage criteria for neural networks could be misleading. In 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 89–92. IEEE, 2019. [16] L. Ma, F. Juefei-Xu, F. Zhang, J. Sun, M. Xue, B. Li, C. Chen, T. Su, L. Li, Y. Liu, et al. Deepgauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, pages 120–131, 2018. [17] L. Ma, F. Zhang, J. Sun, M. Xue, B. Li, F. Juefei-Xu, C. Xie, L. Li, Y. Liu, J. Zhao, et al. Deepmutation: Mutation testing of deep learning systems. In 2018 IEEE 29th international symposium on software reliability engineering (ISSRE), pages 100–111. IEEE, 2018. [18] X. Meng, S. Kundu, A. K. Kanuparthi, and K. Basu. Rtl-contest: Concolic testing on rtl for detecting security vulnerabilities. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(3):466–477, 2021. [19] S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard. Universal adversarial perturbations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1765–1773, 2017. [20] S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard. Deepfool: a simple and accurate method to fool deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2574–2582, 2016. [21] A. Nguyen, J. Yosinski, and J. Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 427–436, 2015. [22] K. Pei, Y. Cao, J. Yang, and S. Jana. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles, pages 1–18, 2017. [23] K. Sen. Concolic testing. In Proceedings of the 22nd IEEE/ACM international conference on Automated software engineering, pages 571–572, 2007. [24] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484–489, 2016. [25] Y. Sun, X. Huang, D. Kroening, J. Sharp, M. Hill, and R. Ashmore. Deepconcolic: Testing and debugging deep neural networks. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pages 111–114. IEEE, 2019. [26] Y. Sun, M. Wu, W. Ruan, X. Huang, M. Kwiatkowska, and D. Kroening. Concolic testing for deep neural networks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pages 109–119, 2018. [27] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013. [28] X. Xie, T. Li, J. Wang, L. Ma, Q. Guo, F. Juefei-Xu, and Y. Liu. Npc: N euron p ath c overage via characterizing decision logic of deep neural networks. ACM Transactions on Software Engineering and Methodology (TOSEM), 31(3):1–27, 2022. [29] X. Xie, L. Ma, F. Juefei-Xu, M. Xue, H. Chen, Y. Liu, J. Zhao, B. Li, J. Yin, and S. See. Deephunter: a coverage-guided fuzz testing framework for deep neural networks. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 146–157, 2019. [30] X. Xu, J. Chen, J. Xiao, Z. Wang, Y. Yang, and H. T. Shen. Learning optimization-based adversarial perturbations for attacking sequential recognition models. In Proceedings of the 28th ACM international conference on multimedia, pages 2802–2822, 2020. [31] Z. Zhou, W. Dou, J. Liu, C. Zhang, J. Wei, and D. Ye. Deepcon: Contribution coverage testing for deep learning systems. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 189–200. IEEE, 2021.; G0110356043; https://nccur.lib.nccu.edu.tw//handle/140.119/149471; https://nccur.lib.nccu.edu.tw/bitstream/140.119/149471/1/604301.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/149471
https://nccur.lib.nccu.edu.tw/bitstream/140.119/149471/1/604301.pdf

المؤلفون: 陳冠宇

المساهمون: 國立臺灣科技大學資訊工程系

مصطلحات موضوعية: 語音文件, 摘要, 多樣性, 重複性, 神經網路模型, Spoken Document, Summarization, Diversity, Redundancy, Neural Modeling

وصف الملف: 117 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/80633; http://ir.lib.ntust.edu.tw/bitstream/987654321/80633/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/80633
http://ir.lib.ntust.edu.tw/bitstream/987654321/80633/1/index.html

المؤلفون: 邱千泰, CHIU, CHIEN-TAI

المساهمون: 謝明華

مصطلحات موضوعية: 數位轉型, 卷積神經網路模型, 印刷業

Relation: Ali, O., Ally, M., & Dwivedi, Y. (2020). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102–199.\r\nBellman, R., & Lee, E. S. (1978). Functional equations in dynamic programming. Aequationes Mathematicae, 17(1), 1–18.\r\nBerman, S. J. (2012). Digital transformation: opportunities to create new business models. Strategy & Leadership.\r\nBharadwaj, A., Sawy, O. A. El, Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 471–482.\r\nBredt, S. (2019). Artificial Intelligence (AI) in the financial sector—Potential and public strategies. Frontiers in Artificial Intelligence, 2, 16.\r\nChan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. F. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156–162.\r\nClarke, D., Puthiyamadam, T., Gaynor, P., & Likens, S. (2020). Payback ahead. Take charge of your future. PwC.\r\nDownes, L., & Nunes, P. (2013). Big-bang disruption. Harvard Business Review, 91(3), 44–56.\r\nFukushima., K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36, 193–202.\r\nHaugeland, J. (1985). Artificial intelligence: the very idea. In: Cambridge, MA: MIT Press.\r\nHeavin, C., & Power, D. J. (2018). Challenges for digital transformation–towards a conceptual decision support guide for managers. Journal of Decision Systems, 27(sup1), 38–45.\r\nMachinery, C. (1950). Computing machinery and intelligence-AM Turing. Mind, 59(236), 433.\r\nMatt, C., Hess, T., & Benlian, A. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), 339–343.\r\nRelewicz, J. Q. (2017). Big data and big money: The role of data in the financial sector. IT Professional, 19(3), 8–10.\r\nRojers, J. P. (2018). Digital Transformation, Business Model Innovation and Efficiency in Content Industries: A Review. The International Technology Management Review, 7(1), 59–70.\r\nSchwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388–393.\r\nShaughnessy, H. (2018). Creating digital transformation: strategies and steps. Strategy & Leadership.\r\nShaw, C., & Hamilton, R. (2016). The intuitive customer: 7 imperatives for moving your customer experience to the next level.\r\nVillalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., & Wellbrock, W. (2019). Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors, 19(18), 3987.\r\nWerbos., P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD Thesis, Harvard University.\r\nWesterman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press.; G0105932007; https://nccur.lib.nccu.edu.tw//handle/140.119/139146; https://nccur.lib.nccu.edu.tw/bitstream/140.119/139146/1/index.html

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/139146
https://doi.org/10.6814/NCCU202200322
https://nccur.lib.nccu.edu.tw/bitstream/140.119/139146/1/index.html

المؤلفون: 黃舒平, Huang, Shu-Ping

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 新能源汽車, 生產效率, 資料包絡分析法, 神經網路模型, New Energy Vehicles, Productive Efficiency, DEA, Neural Network

وصف الملف: 1403721 bytes; application/pdf

Relation: Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Vol. 30, No. 9, pp. 1078-1092.\nCharnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2 (1978), pp. 429-444.\nFarrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), Vol. 120, No. 3, pp. 253-290.\nFäre, R., Grosskopf, S., Norris, M., & Zhang, Z.Y. (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. The American Economic Review, Vol. 84, No. 1, pp. 66-83.\nFried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis. Journal of Productivity Analysis, 17, pp. 157–174.\nJondrow, J., Lovell, C., Materov, I.S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, vol. 19, issue 2-3, pp. 233-238.\nLeibenstein, H. (1966). Allocative Efficiency vs. "X-Efficiency". The American Economic Review, Vol. 56, No. 3, pp. 392-415.\nMcCulloch, W.S., & Pitts, W. (1966). A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biophysics, vol. 5, pp. 115–133.\nWidrow, B, & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE WESCON Convention Record, 1960, pp. 96-104.\n盛士能（2018）。新能源汽車發展概述與趨勢。科學技術創新，2018.23，167-168。\n李大元（2011）。低碳經濟背景下我國新能源汽車產業發展的對策研究。科學經濟縱橫，2011年第2期，72-75。\n李蘇秀、劉穎綺、王靜宇、張雷（2016）。基於市場表現的中國新能源汽車產業發展政策剖析。中國人口·資源與環境，第26卷第9期，158-166。\n孫紅霞、呂慧榮（2018）。新能源汽車後補貼時代政府與企業的演化博弈分析。戰略與決策，第32卷第2期，24-49。\n丁 芸、張天華（2014）。促進新能源汽車產業發展的財稅政策效應研究。稅務研究，第355期，16-20。\n李 磊（2018）。政府研發補貼對新能源汽車產業技術創新產出的影響研究。科技管理研究，第17期。\n李素梅、陳琛、徐繼明（2016）。我國新能源汽車產業融資效率評價與分析——基於DEA-Logit模型的實證研究。科技管理研究，第18期。\n程驍凡、楊凱雯、潘志洋、譚江龍（2021）。中國新能源汽車產業發展現狀及對策。合作經濟與科技，第11期，20-22。\n陳巍巍、張 雷、馬鐵虎、劉秋〇（2014）。關於三階段DEA模型的幾點研究。系統工程，第32卷第9期（總第249期），144-149。\n李娜（2020）。我國跨境電商產業可持續發展的效率評價研究—基於三階段DEA模型實證分析。科技與經濟，第33卷第1期（總第193期），106-110。\n宋馬林、王舒鴻、汝慧萍、張廷海（2010）。中國新興生物企業的生產效率及其不確定性—基於DEA和神經網路類比的面板資料分析。企業管理，2010.10，131-137。; G0108352037; https://nccur.lib.nccu.edu.tw//handle/140.119/138888; https://nccur.lib.nccu.edu.tw/bitstream/140.119/138888/1/203701.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/138888
https://doi.org/10.6814/NCCU202200102
https://nccur.lib.nccu.edu.tw/bitstream/140.119/138888/1/203701.pdf

المؤلفون: 林榮慶, 張達元

المساهمون: 機械系

مصطلحات موضوعية: Neural networks, Taguchi's method, Metal shearing, 田口法, IC導線架橋帶, 剪切參數, 類神經網路模型

وصف الملف: 349 bytes; text/html

Relation: 中國機械工程學刊 29:5 2008.10[民97.10] 頁355-364; http://ir.lib.pccu.edu.tw//handle/987654321/35846; http://ir.lib.pccu.edu.tw/bitstream/987654321/35846/2/index.html

الاتاحة: http://ir.lib.pccu.edu.tw//handle/987654321/35846
http://ir.lib.pccu.edu.tw/bitstream/987654321/35846/2/index.html

المؤلفون: 黃家儀, Huang, Chia-Yi

المساهمون: 柯佳伶, Koh, Jia-Ling

مصطلحات موضوعية: 分群模型, 廣告點擊預測, 互信息值, 類神經網路模型架構, group model, advertising click prediction, mutual information, neural network

وصف الملف: application/pdf

Relation: G060447012S; http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060447012S%22.&%22.id.&; http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/106445

الاتاحة: http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/106445
https://hdl.handle.net/20.500.12235/106445
http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060447012S%22.&%22.id.&

المؤلفون: 朱君亞, Chu, Chun-Ya

المساهمون: 林士貴 蔡炎龍, Lin, Shih-Kuei Tsai, Yen-Lung

مصطلحات موضوعية: 金融情勢指數, 金融壓力事件預警模型, 羅吉斯迴歸模型, 類神經網路模型, 支援向量機, Financial conditions Index, Financial stress model, Logistic regression, Artificial neural network, Support vector machine, eco, manag

Relation: http://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf; http://nccur.lib.nccu.edu.tw//handle/140.119/118357

الاتاحة: http://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf
http://nccur.lib.nccu.edu.tw//handle/140.119/118357

المؤلفون: 饒宇軒, Jao, Yu-Hsuan

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 倒傳遞類神經網路模型, 信用評等, KMV模型, 違約距離, Back propagation neural network model, Credit rating, KMV model, Distance to default, manag, eco

Relation: http://nccur.lib.nccu.edu.tw//handle/140.119/118238

الاتاحة: http://nccur.lib.nccu.edu.tw//handle/140.119/118238

المؤلفون: 唐寧, Tang,Ning

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 選擇權定價, 深度學習, 神經網路模型, Option pricing, Deep learning, Neural network model, manag, eco

Relation: http://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf; http://nccur.lib.nccu.edu.tw//handle/140.119/118537

الاتاحة: http://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf
http://nccur.lib.nccu.edu.tw//handle/140.119/118537

المؤلفون: 唐寧, Tang,Ning, Tang, Ning

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 選擇權定價, 深度學習, 神經網路模型, Option pricing, Deep learning, Neural network model

Relation: G0105352041; http://nccur.lib.nccu.edu.tw//handle/140.119/118537; http://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf

الاتاحة: http://nccur.lib.nccu.edu.tw//handle/140.119/118537
http://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf

المؤلفون: 郭泓霆, Kuo, Hung-Ting

المساهمون: 徐士勛

مصطلحات موضوعية: 匯率預測, 樣本外預測, 神經網路模型

وصف الملف: 3817751 bytes; application/pdf

Relation: G0105258034; http://nccur.lib.nccu.edu.tw//handle/140.119/118813; http://nccur.lib.nccu.edu.tw/bitstream/140.119/118813/1/803401.pdf

الاتاحة: http://nccur.lib.nccu.edu.tw//handle/140.119/118813
http://nccur.lib.nccu.edu.tw/bitstream/140.119/118813/1/803401.pdf

المؤلفون: 朱君亞, Chu, Chun-Ya

المساهمون: 林士貴 蔡炎龍, Lin, Shih-Kuei Tsai, Yen-Lung

مصطلحات موضوعية: 金融情勢指數, 金融壓力事件預警模型, 羅吉斯迴歸模型, 類神經網路模型, 支援向量機, Financial conditions Index, Financial stress model, Logistic regression, Artificial neural network, Support vector machine

وصف الملف: 1585448 bytes; application/pdf

Relation: G0105352011; http://nccur.lib.nccu.edu.tw//handle/140.119/118357; http://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf

الاتاحة: http://nccur.lib.nccu.edu.tw//handle/140.119/118357
http://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf

المؤلفون: 饒宇軒, Jao, Yu-Hsuan

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 倒傳遞類神經網路模型, 信用評等, KMV模型, 違約距離, Back propagation neural network model, Credit rating, KMV model, Distance to default

Relation: G0105352016; http://nccur.lib.nccu.edu.tw//handle/140.119/118238

الاتاحة: http://nccur.lib.nccu.edu.tw//handle/140.119/118238

المؤلفون: 郭泓霆, Kuo, Hung-Ting

المساهمون: 徐士勛

مصطلحات موضوعية: 匯率預測, 樣本外預測, 神經網路模型

وصف الملف: 3817751 bytes; application/pdf

Relation: [1] Adya, M., and Collopy, F. (1998). “How effective are neural networks at forecasting and prediction ? A review and evaluation”. Journal of forecasting J. Forecast., 17, 481–495.\n[2] Bao, W., Yue, J., and Rao, Y. (2017). “A deep learning framework for financial time series using stacked autoencoders and long-short term-memory”, PLOS ONE, 12(7).\n[3] Bengio, Y., Simard, P., and Frasconi, P. (1994). “Learning long-term dependencies with gradient descent is difficult”, Neural Networks, 5(2),157-166\n[4] Caire, P., Hatabian, G. and Muller, C. (1992). “Progress in forecast- ing by neural networks”. Neural Networks, 2, 540-545.\n[5] Connor, J., Martin, R., and Atlas, L. (1994). “Recurrent neural net- works and robust time series prediction” . Neural Networks, 5(2), 240–254.\n[6] Contribution, O. (1989). “On the approximate realization of contin- uous mappings by neural networks”. Neural Networks, 2, 183–192.\n[7] Cybenko, G. (1989). “Approximation by superpositions of a sigmoidal function”. Mathematics of Control, Signals, and Systems, 2, 303–314.\n[8] Enders, W. (2014). Applied econometric time series, 4th Edition. New York, United States : Wiley.\n[9] Granger, C.W.J., and A.P. Andersen. (1978). An introduction to bi- linear time series models (Vandenhoeck and Ruprecht, GSttingen).\n[10] Graves, A. (2012). Supervised sequence labeling with recurrent neural networks. Berlin, Germany : Springer.\n[11] Hornik, K. (1991). “Approximation capabilities of muitilayer feedfor- ward networks”. Neural Networks, 4(2), 251–257.\n[12] Hornik, K. (1993). “Some new results on neural network approxima- tion”. Neural Networks, 6(8), 1069-1072.\n[13] Hornik, K. Stinchcombe, M., and White, H. (1989). “Multilayer feed- forward networks are universal approximators”. Neural Networks, 2(5), 359–366.\n[14] Kim, T. Y., Oh, K. J., Kim, C., and Do, J. D. (2004). “Artificial neu- ral networks for non-stationary time series”. Neurocomputing, 61(1– 4), 439-447.\n[15] Kingma, D. P., and Ba, J. L. (2015). “Adam A method for stochastic optimization”. ICLR, 1-15.\n[16] Kuan, C. (2006). Artificial neural networks. IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.\n[17] Lipton, Z. C., Berkowitz, J., and Elkan, C. (2015). A crit- ical review of recurrent neural networks for sequence learning. arXiv.1506.00019[cs.LG]\n[18] Refenes A. N. , M. Azema-Barac, L. Chen, and S. A. Karoussos. (1993). “Currency exchange rate prediction and neural network design Strategies”. Neural Comput Applic, 1(1), 46-58\n[19] Tong, K., and Lim, K. S. (1980). “Threshold autoregression, limit cycles and cyclical data”. Royal Statistical Society, 42(3), 245-292.\n[20] Vincent, P. (2010). Stacked denoising autoencoders : learning useful representations in a deep network with a local denoising criterion, Paper presented at the 27th International Conference on Machine Learning, 11, 3371–3408.\n[21] Weigend, A.S., Huberman, B.A. and Rumelhart, D.E., (1992). Pre- dicting sunspots and exchange rates with connectionist networks. In:\nM. Casdagli and S. Eubank (Editors), Nonlinear Modelling and Fore- casting, SFI Studies in the Sciences of Complexity, Proc. Vol. XII. Addison-Wesley, Redwood City, pp. 395-432.\n[22] Zhang, G., Patuwo, E. B., Hu, M. Y. (1998). “Forecasting with arti- ficial neural networks : The state of the art”. International Journal of Forecasting, 14, 35–62.; G0105258034; https://nccur.lib.nccu.edu.tw//handle/140.119/118813; https://nccur.lib.nccu.edu.tw/bitstream/140.119/118813/1/803401.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/118813
https://doi.org/10.6814/THE.NCCU.ECONO.010.2018.F06
https://nccur.lib.nccu.edu.tw/bitstream/140.119/118813/1/803401.pdf

المؤلفون: 唐寧, Tang,Ning, Tang, Ning

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 選擇權定價, 深度學習, 神經網路模型, Option pricing, Deep learning, Neural network model

Relation: [1] 李沃墻.(2000).台股重設型權證的評價績效比較陰.真理財金學\r\n報,91-112\r\n[2] 周大鵬. (2008). 基於B-P神經網路的期權定價研究.\r\n(Doctoral dissertation,中國人民大學).\r\n[3] 馬發強. (2012). 基於RBF神經網路的期權定價研究.\r\n(Doctoral dissertation, 中南大學).\r\n[4] 張鴻彥, & 林輝. (2007). 基于小波神經網絡的期權定價模型.\r\n東南大學學報 (自然科學版), 37(4), 716-720.\r\n[5] 董瑩, 烏日嘎, & 齊淑華. (2013). 基於bp神經網路的期權定\r\n價模型. 魯東大學學報(自然科學版), 29(3), 196-199.\r\n[6] 劉志强. (2005). 基于神經網路的期權定價模型. (Doctoral\r\ndissertation, 重慶大學).\r\n[7] 劉旭彬. (2011). 基於神經網路方法的期權定價應用研究.\r\n(Doctoral dissertation, 暨南大學).\r\n[8] 譚朵朵. (2008). 基於bp神經網路的s&p500指數期權定價. 統\r\n計與資訊理論壇, 23(11), 40-43.\r\n[9] Amilon, H. (2003). A neural network versus\r\nblack– scholes: a comparisonof pricing and hedging\r\nperformances.Journal of Forecasting, 22(4), 317-335.\r\n[10] Gençay, R., & Qi, M. (2001). Pricing and hedging\r\nderivative securities with neural networks: bayesian\r\nregularization, early stopping, and bagging. IEEE\r\nTrans Neural Netw, 12(4), 726-734.\r\n[11] Hinton, G. E. (2012). A practical guide to training\r\nrestricted Boltzmann machines. In Neural networks:\r\nTricks of the trade(pp. 599-619). Springer, Berlin,\r\nHeidelberg.\r\n[12] Huang, S. C., & Wu, T. K. (2006, September). A hybrid\r\nunscented Kalman filter and support vector machine\r\nmodel in option price forecasting. In International\r\nConference on Natural Computation (pp. 303-312).\r\nSpringer, Berlin, Heidelberg.\r\n[13] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A\r\nfast learning algorithm for deep belief nets. Neural\r\ncomputation, 18(7), 1527-1554.\r\n[14] Hutchinson, J. M., Lo, A. W., & Poggio, T. (1994). A\r\nnonparametric approach to pricing and hedging\r\nderivative securities via learning networks. Journal\r\nof Finance, 49(3), 851-889.\r\n[15] Liang, X., Zhang, H., Xiao, J., & Chen, Y. (2009).\r\nImproving option price forecasts with neural networks\r\nand support vector regressions. Neurocomputing,\r\n72(13), 3055-3065.\r\n[16] Panayiotis, A. C., Spiros, M. H., & Chris, C. (2004,\r\nJuly). Option pricing and trading with artificial\r\nneural networks and advanced parametric models with\r\nimplied parameters. In Neural Networks, 2004.\r\nproceedings. 2004 IEEE International Joint Conference\r\non (Vol. 4, pp. 2741-2746). IEEE.\r\n[17] Park, H., Kim, N., & Lee, J. (2014). Parametric models\r\nand non-parametric machine learning models for\r\npredicting option prices: empirical comparison study\r\nover kospi 200 index options. Expert Systems with\r\nApplications, 41(11), 5227-5237.\r\n[18] Paul R. Lajbcygier, & Jerome T. Connor. (1997).\r\nImproved option pricing using artificial neural\r\nnetworks and, bootstrap methods. International Journal\r\nof Neural Systems, 8(04), 457-471.\r\n[19] Rumelhart, D. E., Hinton, G. E., & Williams, R. J.\r\n(1986). Learning representations by back-propagating\r\nerrors. nature, 323(6088), 533.\r\n[20] Srivastava, R. K., Greff, K., & Schmidhuber, J.\r\n(2015). Highway networks.\r\narXiv preprintarXiv:1505.00387.\r\n[21] Wang, Y. H. (2009). Nonlinear neural network\r\nforecasting model for stock index option price: Hybrid\r\nGJR–GARCH approach. Expert Systems with Applications,\r\n36(1), 564-570.\r\n[22] Wu, S., Zhong, S., & Liu, Y. (2018). Deep residual\r\nlearning for image steganalysis. Multimedia tools and\r\napplications, 77(9), 10437-10453.; G0105352041; https://nccur.lib.nccu.edu.tw//handle/140.119/118537; https://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/118537
https://doi.org/10.6814/THE.NCCU.MB.014.2018.F06
https://nccur.lib.nccu.edu.tw/bitstream/140.119/118537/1/204101.pdf

المؤلفون: 朱君亞, Chu, Chun-Ya

المساهمون: 林士貴 蔡炎龍, Lin, Shih-Kuei Tsai, Yen-Lung

مصطلحات موضوعية: 金融情勢指數, 金融壓力事件預警模型, 羅吉斯迴歸模型, 類神經網路模型, 支援向量機, Financial conditions Index, Financial stress model, Logistic regression, Artificial neural network, Support vector machine

وصف الملف: 1585448 bytes; application/pdf

Relation: 中文文獻\n[1] 王翎聿（2015），應用倒傳遞類神經網路與支援向量機預測加權股價指數，國防大學管理學院財務管理學系碩士班碩士論文。\n[2] 呂奇傑、李天行、高人龍、黃敏菁（2009），支援向量機與支援向量迴歸於財務時間序列預測之應用，數據分析，第4卷第2期，35-56。\n[3] 張天惠（2012），我國金融情勢指數與總體經濟預測，〈中央銀行季刊〉，第34卷第2期，11-42。\n[4] 黃華山與邱一薰（2005）類神經網路預測台灣50 股價指數之研究，資訊、科技與社會學報，第5卷第2期，19-42。\n[5] 葉怡成（2003），類神經網路模式應用與實作，臺北市：儒林。\n英文文獻\n[1] Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273-297.\n[2] Gauthier, C., Graham, C., and Liu, Y. (2004). Financial Conditions Indexes for Canada. Bank of Canada Working Paper 2004, 22.\n[3] Goodhart, C. and Hofmann, B. (2001). Asset Prices, Financial Conditions, and the Transmission of Monetary Policy. Paper prepared for the conference on Asset Prices, Exchange rates, and Monetary Policy, Stanford University, March 2-3.\n[4] Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., and Watson, M. W. (2010). Financial Conditions Index: A Fresh Look after the Financial Crisis. NBER Working Paper 16150.\n[5] Hsieh, L. F., Hsieh, S. C., and Tai, P. H. (2011). Enhanced Stock Price Variation Prediction via DOE and BPNN-based Optimization. Expert Systems with Applications 38, 14178-14184.\n[6] Hsu, C. W., Chang, C. C., and Lin, C. J. (2003). A Practical Guide to Support Vector Classification. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.\n[7] Huang, W., Nakamoria, Y., and Wang, S. Y. (2004). Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operations Research 32, 2513-2522.\n[8] Kara, Y., Boyacioglu, M. A., and Baykan, Ö . K. (2011). Predicting Direction of Stock Price Index Movement using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange. Expert Systems with Applications 38, 5311-5319.\n[9] Kim, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing 55, 307-319.\n[10] Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning Representations by Back-propagating Errors. Nature, 323, 533-536.\n[11] Silvers, D. and Slavkin, H. (2009). The Legacy of Deregulation and the Financial Crisis: Linkages Between Deregulation in Labor Markets, Housing Finance Markets, and the Broader Financial Markets. Journal of Business & Technology Law 4, 2, 301.\n[12] Skaarup, M., Duschek-Hansen, C., and Nielsen, S. (2010). A Financial Conditions Index for Denmark. Working Paper no 23/2010, The Danish Ministry of Finance.\n[13] Svozil, D., KvasniEka, V., and Pospichal, J. (1997). Introduction to Multi-layer Feed-Forward Neural Networks. Chemometrics and Intelligent Laboratory Systems 39, 43-62.; G0105352011; https://nccur.lib.nccu.edu.tw//handle/140.119/118357; https://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/118357
https://doi.org/10.6814/THE.NCCU.MB.013.2018.F06
https://nccur.lib.nccu.edu.tw/bitstream/140.119/118357/1/201101.pdf

المؤلفون: 饒宇軒, Jao, Yu-Hsuan

المساهمون: 廖四郎, Liao, Szu-Lang

مصطلحات موضوعية: 倒傳遞類神經網路模型, 信用評等, KMV模型, 違約距離, Back propagation neural network model, Credit rating, KMV model, Distance to default

Relation: 一、中文部分\n王濟川、郭志剛，(2003)。Logistic迴歸模型-方法及應用，台北市：\n五南圖書。\n古永嘉、陳達新、陳維寧、楊延福，(2007)。以會計資訊衡量企業信用\n風險：區別分析與類神經網路模型之比較與應用。管理科學研究\n期刊，第四卷，第一期，第39-56頁。\n台灣金融研訓院編輯委員會，(2013)。巴賽爾資本協定三(Basel III)\n實務應用。台北市：台灣金融研訓院。\n朱竣平(2006)。信用評等對公司違約率及財務危機預測之研究。真理大學\n財經研究所碩士論文。\n洪明欽、張揖平、陳昱陵、陳和貴，(2007)。信用評分模型區別力之\n穩健性研究。金融風險管理季刊，第三卷，第四期，第1-23頁。\n陳達新、周恆志，(2014)。財務風險管理(三版)：工具、衡量與未來\n發展。台北：雙葉書廊。\n許峻賓(2004)。KMV模型於預警系統之實證研究。真理大學財經研究所碩士\n論文。\n張大成、林郁翎、林修逸，(2007)。應用市場資訊於企業危機預警之\n研究。運籌與管理學刊，6(1)，1-18。\n黃明祥、許光華、黃榮彬、陳鈺玲，(2005)。KMV模型在台灣金融機構信用\n風險管理機制有效性之研究。財金論文叢刊，第三期，第29-50頁。\n單良、蒙志偉、郭姣君、王慧喧，(2010)。信用評等模型的12堂課—以消費\n金融為例。台北市：台灣金融研訓院。\n葉怡成，(2001)。應用類神經網路。台北：儒林圖書有限公司。\n羅聖雅(2006)。台灣地區上市公司信用風險衡量與績效評估。銘傳大學\n經濟學系碩士在職專班碩士論文。\n蘇敏賢、林修葳，(2006)。Merton模型預測違約之使用限制探索。金融\n風險管理季刊，第二捲，第三期，第65-87頁。\n二、英文部分\nAltman, E. I. (1968). “Financial Ratios, Discriminant\nAnalysis and the Prediction of Corporate Bankruptcy.”\nJournal of Finance, 23(4), 589-609.\nAltman, E. I., Haldeman, R.G., and Narayanan, P.\n(1977). “Zeta Analysis—A New Model to Indentify\nBankruptcy Risk of Corporations.” Journal of Banking\nand Finance, Vol.1, 29-54.\nAtiya, A. F. (2001). “Bankruptcy Prediction for Credit Risk\nUsing Neural Networks: Asurvey and New Results.” IEEE\nTransactions on Neural Networks, 12(4),\n929-935.\nBeaver, W. H. (1966). “Financial Ratios as Predictors of\nFailure.” Journal of Accounting Research, 4, 71-111.\nBlack, F. and Scholes M. (1973). “The Pricing of Options\nand Corporate Liabilities.” Journal of Political\nEconomy, 81(3), 637-654.\nFitzpartrick, P. J. (1932).“A Comparison of Ratios of\nSuccessful Industrial Enterprises with Those of Failed\nFirms.” Certified Public Accountant, 3: 656-662.\nLeshno, M. and Spector Y. (1996). “Neural Network\nPrediction Analysis: The Bankruptcy Case.”\nNeurocomputing, 10(2), 125-147.\nMerton, R. C. (1973). “Theory of Rational Option Pricing.”\nBell Journal of Economics and Management Science 4,\n141-183.\nMerton, R. C. (1974). “On the Pricing of Corporate Debt:\nThe Risk Structure of Interest Rates.” Journal of\nFinance, 29(2), 449-470.\nOhlson, J. A. (1980). “Financial Ratios and the\nProbabilistic Prediction of Bankruptcy.” Journal of\nAccounting Research, 18(1), 109-131.\nZmijewski, M. E. (1984). “Methodological Issues Related to\nthe Estimation of Financial Distress Prediction\nModels.” Journal of Accounting Research, 22,\n59-82.; G0105352016; https://nccur.lib.nccu.edu.tw//handle/140.119/118238

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/118238
https://doi.org/10.6814/THE.NCCU.MB.008.2018.F06

المؤلفون: 陳芊邑, Cian-Yi Chen

المساهمون: 應用經濟學系所, 張嘉玲, Chia-Lin Chang

مصطلحات موضوعية: GARCH, 類神經網路模型, 預測區間, Neural network, Prediction interval, manag, eco

Relation: http://hdl.handle.net/11455/97264

الاتاحة: http://hdl.handle.net/11455/97264

المؤلفون: 陳芊邑, Cian-Yi Chen

المساهمون: 應用經濟學系所, 張嘉玲, Chia-Lin Chang

مصطلحات موضوعية: GARCH, 類神經網路模型, 預測區間, Neural network, Prediction interval

Relation: http://hdl.handle.net/11455/97264

الاتاحة: http://hdl.handle.net/11455/97264

المؤلفون: 杜昱音, TU, YU-YIN

المساهمون: 社會科學院: 國家發展研究所, 指導教授: 邱鳳臨, 杜昱音, TU, YU-YIN

مصطلحات موضوعية: 大陸旅客來臺, SARIMA模型, 季節GM(1,1)模型, 倒傳遞類神經網路模型, 觀光需求預測, SARIMA model, seasonal GM(1,1) model, BNP model, tourism demand, geo, manag

Time: 67

Relation: http://ntur.lib.ntu.edu.tw/bitstream/246246/274663/1/ntu-105-R01341015-1.pdf; http://ntur.lib.ntu.edu.tw/handle/246246/274663

الاتاحة: http://ntur.lib.ntu.edu.tw/bitstream/246246/274663/1/ntu-105-R01341015-1.pdf
http://ntur.lib.ntu.edu.tw/handle/246246/274663