PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2018 | nr 3 (25) | 9--15
Tytuł artykułu

Application of Deep Learning Methods in Management

Autorzy
Treść / Zawartość
Warianty tytułu
Zastosowanie metod deep learning w zarządzaniu
Języki publikacji
EN
Abstrakty
Znalezienie bardziej skutecznego rozwiązania i narzędzi do dużych zbiorów danych w problemach zarządzania jest jednym z najważniejszych i dominujących trendów w badaniach nad zarządzaniem. Wraz z rozwojem komputerów, technologii komunikacyjnych, a zwłaszcza sztucznej inteligencji, narzędzia wykorzystywane do podejmowania decyzji dotyczących zarządzania przeszły zmianę od prostych algorytmów do wielu warstw sieci neuronowych. W dzisiejszych czasach algorytmy Deep Learning (DL) są jednymi z najbardziej wydajnych narzędzi, które mogą stać się kluczowym elementem Business Intelligence. W artykule zostaną opisane podstawowe metody głębokiego uczenia się oraz przegląd wybranych prac wykonanych w zastosowaniu algorytmów DL w naukach o zarządzaniu. Zostanie opisane proponowane przykładowe rozwiązanie, które można wykonać przy użyciu sieci Deep Belief, jednej z metod DL. (abstrakt oryginalny)
EN
Finding more effective solution and tools for big data in management problems is one of the most important and dominant trends in management studies. With the advancement of computer, communication technology and especially artificial intelligence, the tools that are used for management decisions have undergone a change from simple algorithms to many layer neural network methods. Nowadays Deep learning (DL) algorithms are one of the most efficient tools that may become a critical component of business intelligence. In the paper will be described the basic of deep learning methods as well as a review of selected works done in application of DL algorithms in management sciences. It will be described the proposed exemplary solution, that can be done with use of Deep Belief Network, one of the DL methods. (original abstract)
Twórcy
  • Akademia Wyższej Szkoły Biznesu w Dąbrowie Górniczej
Bibliografia
  • Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., and Baskurt, A. (2011). Sequential deep learning for human action recognition. "International Workshop on Human Behavior Understanding",. Springer 2011, pp. 29-39.
  • Badura, D. (2018). Prediction of urban traffic flow based on generative neural network model, Communication in Computer and Information Science - Springer, pp. 3-17.
  • Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. arXiv: 1206.5533 [cs.LG].
  • Chen, Y. (2017). Integrated and Intelligent Manufacturing: Perspectives and Enablers, "Engineering", 2017, 3, pp. 588-595.
  • Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. "Biological Cybernnetics Springer", 36(4): doi:10.1007/bf00344251. PMID 7370364, pp. 193- 202.
  • Geng, Cui, Man Leung Wong, Hon-Kwong Lui (2006). Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming, "Manegement Science", Vol. 52, No. 4, April 2006, issn0025-1909, eissn 1526-5501 06 5204 0597, pp. 597-612.
  • Graves, A., Eck, D., Beringer, N., Schmidhuber, J. (2003). Biologically Plausible Speech Recognition with LSTM Neural Nets. 1st Intl. Workshop on Biologically Inspired Approaches to Advanced Information Technology, Bio-ADIT 2004, Lausanne, Switzerland, pp.175-184.
  • Graves, A., Fernández, S., Gomez, F. (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the International Conference on Machine Learning, ICML 2006, pp. 369-376.
  • Goller, C., Kuchler, A. (1996). Learning task-dependent distributed representations by backpropagation through structure. In Neural Networks, IEEE International Conference on, volume 1, IEEE, 1996, pp. 347-352.
  • Hinton, A. G., Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. "Science", 313 (5786), July 2006, pp. 504-507.
  • Hinton, G. E.,Osindero, S.,Teh, Y.W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation. 18 (7). doi:10.1162/ neco.2006.18.7.1527. PMID 16764513.pp. 1527-1554.
  • Hinton, G. E. (2007). Learning multiple layers of representation. Trends in "Cognitive Sciences", 11 (10). doi:10.1016/j. tics.2007.09.004. ISSN 1364-6613. PMID 17921042, pp. 428-434.
  • Hochreiter, S., Schmidhuber, J., (1997). Long Short-Term Memory. Neural Computation. 9 (8). doi:10.1162/neco.1997.9.8.1735. ISSN 0899-7667. PMID 9377276, pp. 1735-1780.
  • Hrasko, R., Pacheco, A. G. C., Krohling, R. A. (2015). Time Series Prediction using Restricted Boltzmann Machines and Backpropagation. "Procedia Computer Science" 55 ( 2015 ), pp. 990 - 999.
  • Indranil Bose, Radha, K., Mahapatra. (2001). Business data mining - a machine learning perspective, "Information & Management", Volume 39, Issue 3, 20 December 2001, pp. 211-225.
  • Ivakhnenko, A.G. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, "Man and Cybernetics", Volume: SMC-1, Issue: 4. Oct. 1971. doi:10.1109/TSMC.1971.4308320.pp. 364-378.
  • Krycha, K.A., Wagner, U. (1999). Applications of artificial neural networks in management science: a survey. "Journal of Retailing and Consumer Services", 6, pp. 185-203.
  • Kuremoto, T., Kimura, S., Kobayashi, K., Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. "Neurocomputing" 137 (2014) pp. 47-56.
  • LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard,R.e., Hubbard, W., Jackel, L.D. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. "Neural Computation", 1, pp. 541-551, 1989.
  • LeCun, Y. Benglio, Y., Hinton, G. (2015). Deep learning, Review, "Nature" 14539, vol. 521, 28 May 2015, pp. 436-447. doi:10.1038.
  • Osogami, T., Otsuka, M.(2014). Restricted Boltzmann machines modeling human choice. Advances in Neural Information Processing "Systems" 2014, 27, pp. 73-81. [https:// papers.nips.cc/paper/5280-restricted-boltzmann-machines-modelinghuman-choice.pdf].
  • Salakhutdinov, R., Hinton, G.E. (2009). Deep Boltzmann Machines; Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, Clearwater Beach, Florida, USA. Volume 5 of JMLR:W&CP 5.
  • Sak, H., Senior, A., Beaufays, F. (2014), Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, "INTERSPEECH" 2014, and also: arXiv: 1402.1128 [cs.NE],5 Feb 2014, pp. 338-342.
  • Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. "Parallel Distributed Processing", volume 1, chapter 6,. MIT Press, 1986. pp. 194-281.
  • Sirignano, J., Giesecke, K. (2015). Risk Analysis for Large Pools of Loans, SSRN, December 22, 2015. pp. 1-37.
  • Sirignano, J., Sadhwani, A., Giesecke, K. (2018). Deep Learning for MortgageRisk, arXiv:1607.02470v2[q-fin.ST].
  • Sun, Y., Wang, X., Tang, X. (2009). Hybrid Deep Learning for Face Verification, IEEE "Transactions on Pattern Analysis and Machine Intelligence", vol. 38 Issue: 10, doi: 10.1109/TPAMI.2015.2505293. pp. 1489- 1496.
  • Tan, H., Xuan, X., Wu, Y., Zhong, Z., Ran, B. (2016). A comparison of traffic flow prediction methods based on DBN. Processedings of the 16th COTA International Conference of Transportation Professionals (CICTP), Shanghai, China, 6-9 July 2016; pp. 273-283.
  • Wang, J., Ma,Y., Zhang, L., XGao, DazhongWu R. (2018). Deep learning for smart manufacturing: Methods and applications, "Journal of Manufacturing Systems": (Available online 8 January 2018).
  • Widergren, P. (2017). Deep learning-based forecasting of financial assets, KTH Royal Institute of Technology School of Engineering Science, Degree Project in Mathematics, Second Cycle, 30 Credits Stockholm, Sweden 2017, pp. 1-70.
  • Zahra, S.A., Ireland, R.D., Hitt, M.A. (2000). International Expansion by New Venture Firms: International Diversity, Mode of Market Entry, Technological Learning, and Performance, "The Academy of Management Journal" 2000, Vol. 43, No. 5, pp. 925-950.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171560359

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.