|
Alcock, R. J., Manolopoulos, Y., 1999. Time series similarity queries employing a feature-based approach. 7th Hellenic Conference on Informatics. Ioannina, Greece.
Anthony, M., Biggs, N. 1997. Computational Learning Theory. Cambridge University Press.
Aytug, H., Bhattacharyya, S., Koehler, G. J., et al., 1994. A review of machine learning. IEEE Transactions on Engineering Management 41(2), 165–171.
Chen, C. C., Yih, Y., 1996. Identifying attributes for knowledge-based development in dynamic scheduling environments. International Journal Production Research 34(6), 1739–1755.
Chi, H.M., Ersoy, M.K., 2005. A statistical self-organizing learning system for remote sensing classification. IEEE Transactions on Geoscience and Remote Sensing 43, 1890–1900.
Doan, K., Wong, K. P. 1995. Artificial intelligence-based machine-learning system for Thermal generator scheduling. IEE Proceedings-Generation Transmission and Distribution 142(2), 195–201.
Huang, C., Moraga, C., 2004. A diffusion-neural-network for learning from small samples. International Journal of Approximate Reasoning 35, 137–161.
Jang, J. S. R., 1993. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685.
Kadous, M. W., Sammut, C., 2004. Constructive induction for classifying time series. Lecture Notes in Computer Science 3201, 192–204.
Lee, C.Y., Piramuthu, S., Tsai, Y. K., 1997. Job shop scheduling with a genetic algorithm and machine learning. International Journal of Production Research 35(4), 1171–1191.
Li, D. C., Chen, L. S., Lin, Y. S., 2003. Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments. International Journal of Production Research 41(17), 4011–4024.
Li, D. C., Lin, Y. S., 2006b. Using virtual sample generation to build up management knowledge in the early manufacturing stages. European Journal of Operational Research 175(1), 413–434.
Li, D. C., Wu, C., Chang, F. M., 2005. Using Data-fuzzification Technology in Small Data Set Learning to Improve FMS Scheduling Accuracy. International Journal of Advanced Manufacturing Technology 27, 321–328. Li, D. C., Wu, C., Tsia, T. I., and Chang, F. M. 2006a, Using Mega-Fuzzification and Data Trend Estimation in Small Data Set Learning for Early FMS Scheduling Knowledge. Computers & Operations Research 33, 1857–1869.
Lindsay, D., Cox, S., 2005. Effective probability forecasting for time series data using standard machine learning techniques. Lecture Notes in Computer Science, 3686, 35–44.
Mitchell, T. M., 1997. Machine Learning. New York: McGraw-Hill.
Monch, L., Zimmermann, J., Otto, P., 2006. Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines. Engineering Applications of Artificial Intelligence 19(3), 235–245.
Montanes, E., Quevedo, J. R., Prieto, M. M., et al., 2002. Forecasting time series combining machine learning and Box-Jenkins timeseries. Lecture Notes in Artificial Intelligence 2527, 491–499.
Nakasuka, S., Yoshida, T., 1992. Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool. International Journal Production Research 30, 411–431.
Niyogi, P., Girosi, F., Tomaso, P., 1998. Incorporating prior information in machine learning by creating virtual examples. Proceeding of the IEEE, 275–298.
Pierreval, H., Ralambondrainy, H., 1990. A simulation and learning technique for generating knowledge about manufacturing systems behavior. Journal of the Operational Research Society 41(6), 461–474.
Popescu, C. A., Wong Y. S., 2005. Nested Monte Carlo EM algorithm for switching state-space models. IEEE Transactions on Knowledge and Data engineering 17, 1653–1663.
Priore, P., de la Fuente, D., Gomez, A., Puente, J., 2001. A review of machine learning in dynamic scheduling of flexible manufacturing systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, 251–263.
Priore, P., de la Fuente, D., Puente, J., et al., 2006. A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence 19(3), 247–255.
Prudenico, R. B. C., Ludermir, T. B., 2004. Meta-learning approaches to selecting time series models. Neurocomputing 61, 121–137.
Prudenico, R. B. C., Ludermir, T. B., 2004. Using machine learning techniques to combine forecasting methods. Lecture Notes in Artificial Intelligence 3339, 1122–1127.
Quinlan, J. R., 1996. Learning decision tree classifiers. ACM Computing Surveys 28(1), 71–72.
Sabuncuoglu, I., Touhami, S., 2002. Simulation metamodeling with neural networks: an experimental investigation. International Journal of Production Research 40, 2483–2505.
Shaw, M. J., Park, S., Raman, N., 1992. Intelligent scheduling with machine learning capabilities: The induction of scheduling knowledge. IIE Transactions 24(2), 156–168.
Sun, Y. L., Yih, Y., 1996. An intelligent controller for manufacturing cells. International Journal of Production Research 34(8), 2353–2373.
|