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참고문헌 (10)

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3. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. 

4. Duncan, S., Balchanos, M., Sung, W., Kim, J., Li, Y., Issac, Y., & Mavris, D. (2014). Towards a Data Calibrated, Simulation-Based Campus Energy Analysis Environment for Situational Awareness and Future Energy System Planning. Proceedings of the ASME 2014 8th International Conference on Energy Sustainability, 1-10. https://doi.org/10.1115/ES2014-6695 

   

5. Gao, X., Pishdad-Bozorgi, P., Shelden, D. R., & Shu, T. (2019). Machine Learning-based Life-cycle Cost Analysis for Educational Facilities. 55th ASC Annual International Conference, October, 393-400. https://www.researchgate.net/publication/334262496_Machine_Learning-based_Life-cycle_Cost_Analysis_for_Educational_Facilities 

6. Hong, S., Kim, J., & Yang, E. (2020). Automated Text Classification of Historical Maintenance Data of Higher Education Buildings using Text Mining and Machine Learning Methods. Manuscript Submitted for Publication. 

7. Hunt, R. A., Killen, M. C. P., Bosch, V. G., & Enriquez, F. T. (2005). TQM and QFD: exploiting a customer complaint management system. International Journal of Quality & Reliability Management . https://doi.org/https://doi.org/10.1108/02656710510572977 

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8. Mamidi, S., Chang, Y.-H., & Maheswaran, R. (2012). Improving building energy efficiency with a network of sensing, learning and prediction agents. Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, 45-52. 

9. Pyon, C. U., Woo, J. Y., & Park, S. C. (2011). Service improvement by business process management using customer complaints in financial service industry. Expert Systems with Applications, 38(4), 3267-3279. https://doi.org/10.1016/j.eswa.2010.08.112 

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10. Rai, P., Nassif, N., Eaton, K., & Rodrigues, A. (2019). Applications of Machine Learning in Building Energy Prediction and Savings. Energy , 1, 1-2. https://doi.org/10.3844/erjsp.2019 

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