연구동향 분석
박사

기술집약기업의 성과예측을 위한 하이브리드 예측모델 = A hybrid model for predicting corporate performance of technology-intensive firms

이준혁 2018년

논문상세정보
인용/피인용
' 기술집약기업의 성과예측을 위한 하이브리드 예측모델 = A hybrid model for predicting corporate performance of technology-intensive firms' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • dbn
  • svr
  • 기계학습
  • 기업 성과 예측
  • 예측모형
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,201 0

0.0%

' 기술집약기업의 성과예측을 위한 하이브리드 예측모델 = A hybrid model for predicting corporate performance of technology-intensive firms' 의 참고문헌

  • 「머신러닝에서 딥러닝까지」
    곽동민 박세원 이한남 퍼플: 서울 [2015]
  • Yuan, F. Parameters optimization using genetic algorithms in support vector regression for sales volume forecasting. Applied Mathematics. 2012, 3(10), 1480–1486.
  • Yoon, Y.; Swales, G. Predicting stock price performance: A neural network approach. In Proceedings of the IEEE Twenty-Fourth Annual Hawaii International Conference on System Sciences, Kauai, HI, USA, 8–11 January 1991; pp. 156 –162.
  • Yeh, C.; Chi, D.; Hsu, M. A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications. 2010, 37(2), 1535–1541.
  • Urbano, D.; Toledano, N.; Ribeiro-Soriano, D. Socio-cultural factors and transnational entrepreneurship: A multiple case study in Spain. International Small Business Journal. 2011, 29(2), 119–134.
  • Troiano, L.; Kriplani, P. Predicting trend in the next-day market by Hierarchical Hidden Markov Model. In Proceedings of the International Conference on Computer Information Systems and Industrial Management Applications, CISIM, Krackow, Poland, 8–10 October 2010; pp. 199–204.
  • Thoma, G. Composite value index of patent indicators: Factor analysis combining bibliographic and survey data sets. World patent information. 2014, 38, 19–26.
  • Soriano, D.R.; Huarng, K.H. Innovation and entrepreneurship in knowledge industries. Journal of Business Research. 2013, 66(10), 1964–1969.
  • Son, H.; Kim, C.; Kim, C. Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables. Automation in Construction. 2012, 27, 60– 66.
  • Smola, A.J.; Sch lkopf, B. A tutorial on support vector regression. Statistics and computing. 2004, 14(3), 199–222.
  • Shen, F.; Chao, J.; Zhao, J. Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing 2015, 167, 243–253.
  • Shane, H.; Klock, M. The relation between patent citations and Tobin’s Q in the semiconductor industry. Rev. Quant. Financ. Account. 1997, 9, 131–146.
  • Ribeiro, B.; Lopes, N. Deep belief networks for financial prediction. In Neural Information Processing, Proceedings of the International Conference on Neural Information Processing, Shanghai, China, 13–17 November 2011; Springer: Berlin/Heidelberg, Germany, 2011; pp. 766–773.
  • Pascanu, R.; Mikolov, T.; Bengio, Y. On the difficulty of training recurrent neural networks. International Conference on Machine Learning. 2013, 3, 1310–1318.
  • Parellada, F.S.; Soriano, D.R.; Huarng, K.H. An overview of the service industries' future (priorities: Linking past and future). The Service Industries Journal. 2011, 31(1), 1–6.
  • Mohamed, A.R.; Dahl, G.; Hinton, G. Deep belief networks for phone recognition. In Proceedings of the NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, Whistler, BC, Canada, 12 December; p. 39.
  • Mogee, M. Using patent data for technology analysis and planning. Research-Technology Management. 1991, 34(4), 43– 49.
  • Mas-Tur, A.; Soriano, D.R. The level of innovation among young innovative companies: The impacts of knowledge-intensive services use, firm characteristics and the entrepreneur attributes. Service Business. 2014, 8(1), 51–63.
  • Martino, J. Technology Forecasting for Decision Making, 3rd ed.; McGraw-Hill: New York, NY, USA, 1993; pp. 93–96.
  • Li, H.; Sun, J. Predicting business failure using multiple case-based reasoning combined with support vector machine. Expert Systems with Applications. 2009, 36(6), 10085–10096.
  • Lee, J.; Kim, G.; Park, S.; Jang, D. Hybrid corporate performance prediction model considering technical capability. Sustainability. 2016, 8(7), 640.
  • Lanjouw, J.O.; Schankerman, M. Patent quality and research productivity: Measuring innovation with multiple indicators. The Economic Journal. 2004, 114(495), 441–465.
  • Lam, M. Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision support systems. 2004, 37(4), 567–581.
  • Kuremoto, T.; Kimura, S.; Kobayashi, K.; Obayashi, M. Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 2014, 137, 47-56.
  • Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012; pp. 1097–1105.
  • Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; Fei-Fei, L. Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 1725–1732.
  • Jun, S.; Park, S.; Jang, D. Technology forecasting using matrix map and patent clustering. Industrial Management & Data Systems. 2012, 112(5), 786–807.
  • Jin, X.; Zhang, Y.; Yao, D. Simultaneous prediction of network traffic flow based on PCA-SVR. Advances in Neural Networks –ISNN 2007. 2007, 1022–1031.
  • Ince, H.; Trafalis, T.B. Short-term forecasting with support vector machines and application to stock price prediction. International Journal of General Systems. 2008, 37(6). 677–687.
  • Huang, W.; Song, G.; Hong, H.; Xie, K. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems. 2014, 15(5), 2191–2201.
  • Huang, W.; Nakamori, Y.; Wang, S. Forecasting stock market movement direction with support vector machine. Computers & Operations Research. 2005, 32(10), 2513–2522.
  • Huang, S.; Tsai, C.; Yen, D.C.; Cheng, Y. A hybrid financial analysis model for business failure prediction. Expert Systems with Applications. 2008, 35(3), 1034–1040.
  • Hsu, S.; Hsieh, J.; Chih, T.; Hsu, K. A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression. Expert Systems with Applications. 2009, 36(4), 7947–7951.
  • Hinton, G.E.; Osindero, S.; Teh, Y.W. A fast learning algorithm for deep belief nets. Neural computation. 2006, 18(7), 1527–1554.
  • Hall, B.H.; Jaffe, A.; Trajtenberg, M. Market value and patent citations. RAND Journal of economics. 2005, 36, 16–38.
  • Estrella, A.; Mishkin, F.S. Predicting US recessions: Financial variables as leading indicators. The Review of Economics and Statistics. 2006, 80(1), 45–61.
  • Doyle, P.; Fenwick, I. Sales forecasting—Using a combination of approaches. Long Range Planning. 1976, 9(3), 60–64.
  • Del Mar Benavides-Espinosa, M.; Soriano, D.R. Cooperative learning in creating and managing joint ventures. Journal of Business Research. 2014, 67(4), 648–655.
  • Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 2002, 6(2), 182–197.
  • Chen, K.; Wang, C. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management. 2007, 28(1), 215–226.
  • Chen, K. Forecasting systems reliability based on support vector regression with genetic algorithms. Reliability Engineering & System Safety. 2007, 92(4), 423–432.
  • Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 27–29 July 1992; pp. 144–152.
  • Bengio, Y. Learning deep architectures for AI. Foundations and trends in Machine Learning. 2009, 2(1), 1–127.
  • Ang, J.S.; Chua, J.H.; Sellers, R. Generating cash flow estimates: An actual study using the Delphi Technique. Financial Management. 1979, 8, 64–67.
  • Ahn, B.; Cho, S.; Kim, C. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications. 2000, 18(2), 65– 74.