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NTIS 바로가기Electronic markets, v.31 no.3, 2021년, pp.685 - 695
Janiesch, Christian , Zschech, Patrick , Heinrich, Kai
AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep lea...
IEEE Access A Adadi 6 52138 2018 10.1109/ACCESS.2018.2870052 Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052.
International Journal of Hospitality Management A Ahani 80 52 2019 10.1016/j.ijhm.2019.01.003 Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77. https://doi.org/10.1016/j.ijhm.2019.01.003.
Amorós, L., Hafiz, S. M., Lee, K., & Tol, M. C. (2020). Gimme that model!: A trusted ML model trading protocol. arXiv:2003.00610 [cs]. http://arxiv.org/abs/2003.00610
Bastan, M., Ramisa, A., & Tek, M. (2020). Cross-modal fashion product search with transformer-based Embeddings. CVPR Workshop - 3rd workshop on Computer Vision for Fashion, Art and Design, Seattle: Washington.
Bishop, C. M. (2006). Pattern recognition and machine learning (Information science and statistics). Springer-Verlag New York, Inc.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 1-20.
Mathematics and Computers in Simulation SH Chen 78 2-3 379 2008 10.1016/j.matcom.2008.01.028 Chen, S. H., Jakeman, A. J., & Norton, J. P. (2008). Artificial intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and Computers in Simulation, 78(2-3), 379-400. https://doi.org/10.1016/j.matcom.2008.01.028.
Pattern Recognition Letters RPW Duin 15 3 215 1994 10.1016/0167-8655(94)90052-3 Duin, R. P. W. (1994). Superlearning and neural network magic. Pattern Recognition Letters, 15(3), 215-217. https://doi.org/10.1016/0167-8655(94)90052-3.
IEEE/CVF Conference on Computer Vision and Pattern Recognition K Eykholt 2018 1625 2018 10.1109/CVPR.2018.00175 Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018). Robust physical-world attacks on deep learning visual classification. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 1625-1634. https://doi.org/10.1109/CVPR.2018.00175.
Electronic Markets M Fischer 30 1 131 2020 10.1007/s12525-019-00384-5 Fischer, M., Heim, D., Hofmann, A., Janiesch, C., Klima, C., & Winkelmann, A. (2020). A taxonomy and archetypes of smart services for smart living. Electronic Markets, 30(1), 131-149. https://doi.org/10.1007/s12525-019-00384-5.
Missouri S&T’s Peer to Peer DJ Fuchs 2 1 15 2018 Fuchs, D. J. (2018). The dangers of human-like Bias in machine-learning algorithms. Missouri S&T’s Peer to Peer, 2(1), 15.
ACM Computing Surveys J Gama 46 4 1 2014 10.1145/2523813 Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 1-37. https://doi.org/10.1145/2523813.
Journal of Machine Learning Research S García 9 89 2677 2008 García, S., & Herrera, F. (2008). An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. Journal of Machine Learning Research, 9(89), 2677-2694.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
CIRP Journal of Manufacturing Science and Technology D Goyal 10 24 2015 10.1016/j.cirpj.2015.05.004 Goyal, D., & Pabla, B. S. (2015). Condition based maintenance of machine tools-A review. CIRP Journal of Manufacturing Science and Technology, 10, 24-35. https://doi.org/10.1016/j.cirpj.2015.05.004.
Journal of Field Robotics S Grigorescu 37 3 362 2020 10.1002/rob.21918 Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3), 362-386. https://doi.org/10.1002/rob.21918.
Heinrich, K., Graf, J., Chen, J., Laurisch, J., & Zschech, P. (2020). Fool me once, shame on you, fool me twice, shame on me: A taxonomy of attack and defense patterns for AI security. Proceedings of the 28th European Conference on Information Systems (ECIS).
Heinrich, K., Möller, B., Janiesch, C., & Zschech, P. (2019). Is Bigger Always Better? Lessons Learnt from the Evolution of Deep Learning Architectures for Image Classification. Proceedings of the 2019 Pre-ICIS SIGDSA Symposium. https://aisel.aisnet.org/sigdsa2019/20
Decision Support Systems K Heinrich 143 113494 2021 10.1016/j.dss.2021.113494 Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494. https://doi.org/10.1016/j.dss.2021.113494.
Procedia Computer Science A Jayanth Balaji 143 947 2018 10.1016/j.procs.2018.10.340 Jayanth Balaji, A., Harish Ram, D. S., & Nair, B. B. (2018). Applicability of deep learning models for stock Price forecasting an empirical study on BANKEX data. Procedia Computer Science, 143, 947-953. https://doi.org/10.1016/j.procs.2018.10.340.
Science MI Jordan 349 6245 255 2015 10.1126/science.aaa8415 Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415.
Artificial Intelligence Review SB Kotsiantis 26 3 159 2006 10.1007/s10462-007-9052-3 Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159-190. https://doi.org/10.1007/s10462-007-9052-3.
Electronic Markets N Kühl 30 2 351 2020 10.1007/s12525-019-00351-0 Kühl, N., Mühlthaler, M., & Goutier, M. (2020). Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media. Electronic Markets, 30(2), 351-367. https://doi.org/10.1007/s12525-019-00351-0.
10.1038/nature14539 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539.
International Journal of Computer Vision DG Lowe 60 2 91 2004 10.1023/B:VISI.0000029664.99615.94 Lowe, D. G. (2004). Distinctive image features from scale-invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.
10.1038/s41746-017-0013-1 Madani, A., Arnaout, R., Mofrad, M., & Arnaout, R. (2018). Fast and accurate view classification of echocardiograms using deep learning. Npj Digital Medicine, 1(1). https://doi.org/10.1038/s41746-017-0013-1.
Artificial Intelligence T Miller 267 1 2019 10.1016/j.artint.2018.07.007 Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007.
IEEE Access Z Pan 7 36322 2019 10.1109/ACCESS.2019.2905015 Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019). Recent Progress on generative adversarial networks (GANs): A survey. IEEE Access, 7, 36322-36333. https://doi.org/10.1109/ACCESS.2019.2905015.
10.1109/ICMLA.2016.0172 Paula, E. L., Ladeira, M., Carvalho, R. N., & Marzagão, T. (2016). Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering. 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 954-960. https://doi.org/10.1109/ICMLA.2016.0172.
10.25300/MISQ/2020/14458 Pentland, B. T., Liu, P., Kremser, W., & Haerem, T. (2020). The dynamics of drift in digitized processes. MIS Quarterly, 44(1), 19-47. https://doi.org/10.25300/MISQ/2020/14458.
Machine Learning M Peters 92 1 5 2013 10.1007/s10994-013-5340-0 Peters, M., Ketter, W., Saar-Tsechansky, M., & Collins, J. (2013). A reinforcement learning approach to autonomous decision-making in smart electricity markets. Machine Learning, 92(1), 5-39. https://doi.org/10.1007/s10994-013-5340-0.
ACM Computing Surveys S Pouyanfar 51 5 1 2019 10.1145/3234150 Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S. S. (2019). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 1-36. https://doi.org/10.1145/3234150.
Procedia Computer Science S Ramaswamy 140 170 2018 10.1016/j.procs.2018.10.326 Ramaswamy, S., & DeClerck, N. (2018). Customer perception analysis using deep learning and NLP. Procedia Computer Science, 140, 170-178. https://doi.org/10.1016/j.procs.2018.10.326.
Nature Machine Intelligence C Rudin 1 5 206 2019 10.1038/s42256-019-0048-x Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x.
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Information Processing & Management G Salton 24 5 513 1988 10.1016/0306-4573(88)90021-0 Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. https://doi.org/10.1016/0306-4573(88)90021-0.
Neural Networks J Schmidhuber 61 85 2015 10.1016/j.neunet.2014.09.003 Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003.
Behavioral and Brain Sciences JR Searle 3 3 417 1980 10.1017/S0140525X00005756 Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417-424. https://doi.org/10.1017/S0140525X00005756.
Electronic Markets D Selz 30 1 57 2020 10.1007/s12525-019-00393-4 Selz, D. (2020). From electronic markets to data driven insights. Electronic Markets, 30(1), 57-59. https://doi.org/10.1007/s12525-019-00393-4.
10.2307/23042796 Shmueli, G., & Koppius, O. (2011). Predictive analytics in information systems research. Management Information Systems Quarterly, 35(3), 553-572. https://doi.org/10.2307/23042796.
Journal of Business Research YR Shrestha 123 588 2021 10.1016/j.jbusres.2020.09.068 Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588-603. https://doi.org/10.1016/j.jbusres.2020.09.068.
Science D Silver 362 6419 1140 2018 10.1126/science.aar6404 Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science, 362(6419), 1140-1144. https://doi.org/10.1126/science.aar6404.
Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market making via reinforcement learning. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent systems, 434-442. arXiv:1804.04216v1
Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, Kevin, Parkes, D., Press, W., Saxenian, A. L., Shah, J., Milind Tambe, & Teller, A. (2016). Artificial Intelligence and Life in 2030: the one hundred year study on artificial intelligence (Report of the 2015-2016 study panel). Stanford University. https://ai100.stanford.edu/2016-report
10.1109/TSC.2020.3000900 Wang, S., Nepal, S., Rudolph, C., Grobler, M., Chen, S., & Chen, T. (2020). Backdoor attacks against transfer learning with pre-trained deep learning models. IEEE Transactions on Services Computing, 1-1. https://doi.org/10.1109/TSC.2020.3000900.
Wanner, J., Heinrich, K., Janiesch, C., & Zschech, P. (2020). How much AI do you require? Decision factors for adopting AI technology. Proceedings of the 41st International Conference on Information Systems (ICIS).
10.22215/timreview/1282 Westerlund, M. (2019). The emergence of Deepfake technology: A review. Technology Innovation Management Review, 9(11), 39-52. https://doi.org/10.22215/timreview/1282
Machine Learning G Widmer 23 1 69 1996 10.1007/BF00116900 Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1), 69-101. https://doi.org/10.1007/BF00116900.
IEEE Computational Intelligence Magazine T Young 13 3 55 2018 10.1109/MCI.2018.2840738 Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing [review article]. IEEE Computational Intelligence Magazine, 13(3), 55-75. https://doi.org/10.1109/MCI.2018.2840738.
10.1038/s41524-018-0081-z Zhang, Y., & Ling, C. (2018). A strategy to apply machine learning to small datasets in materials science. npj Computational Materials, 4(1). https://doi.org/10.1038/s41524-018-0081-z.
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