Tham khảo tài liệu 'computational intelligence in automotive applications by danil prokhorov_6', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 87 Application of Graphical Models in the Automotive Industry Fig. 5. Although it was not possible to find a reasonable description of the vehicles contained in subsets 3 the attribute values specifying subset 4 were identified to have a causal impact on the class variable Fig. 6. In this setting the user selected the parent attributes manually and was able to identify the subset 5 which could be given a causal interpretation in terms of the conditioning attributes Temperature and Mileage 88 M. Steinbrecher et al. 5 Conclusion This paper presented an empirical evidence that graphical models can provide a powerful framework for data- and knowledge-driven applications with massive amounts of information. Even though the underlying data structures can grow highly complex both presented projects implemented at two automotive companies result in effective complexity reduction of the methods suitable for intuitive user interaction. References 1. R. Agrawal T. Imielinski and . Swami. Mining Association Rules between Sets of Items in Large Databases. In P. Buneman and S. Jajodia editors Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data Washington DC May 26-28 1993 pp. 207-216. ACM Press New York 1993. 2. C. Borgelt and R. Kruse. Some Experimental Results on Learning Probabilistic and Possibilistic Networks with Different Evaluation Measures. In First International Joint Conference on Qualitative and Quantitative Practical Reasoning ECSQARU FAPR 97 pp. 71-85 Bad Honnef Germany 1997. 3. C. Borgelt and R. Kruse. Probabilistic and possibilistic networks and how to learn them from data. In O. Kaynak L. Zadeh B. Turksen and I. Rudas editors Computational Intelligence Soft Computing and Fuzzy-Neuro Integration with Applications NATO ASI Series F pp. 403-426. Springer Berlin Heidelberg New York 1998. 4. C. Borgelt and R. Kruse. Graphical Models - Methods for Data Analysis and Mining. Wiley Chichester 2002. 5. E. Castillo . Gutierrez and . .
