By Csilla Csendes, Sándor Fegyverneki (auth.), Gabriella Bognár, Tibor Tóth (eds.)
The aim of the publication is to provide a range from the papers, which summarize numerous vital effects received in the framework of the József Hatvany Doctoral tuition working on the college of Miskolc, Hungary. in keeping with the 3 major learn components of the Doctoral tuition confirmed for info technology, Engineering and expertise, the papers could be labeled into 3 teams. they're as follows: (1) utilized Computational technology; (2) creation info Engineering (IT for production included); (3) fabric flow structures and IT for Logistics. As regards the 1st sector, a few papers take care of designated problems with algorithms conception and its purposes, with computing algorithms for engineering initiatives, in addition to definite problems with facts base structures and data extensive platforms. with regards to the second one study region, the focal point is on construction info Engineering with specific regard to discrete construction strategies. within the moment learn zone the papers convey a few new built-in structures compatible for optimizing discrete construction procedures in a top-down means. The papers connecting with the 3rd learn box care for various problems with fabrics circulation structures and logistics, considering of utilized mathematical versions and IT-tools. The publication makes an attempt to make sure definite equilibrium among thought and perform and to teach a few new strategy either from theoretical modelling element, in addition to experimental and functional perspective.
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Additional info for Applied Information Science, Engineering and Technology: Selected Topics from the Field of Production Information Engineering and IT for Manufacturing: Theory and Practice
N ) = e k 37 Θk f k (ti ,ti−1 ,wi ) (5) i where Z() is normalization function, f() is the feature function and θ is the parameter vector. The domain of feature engineering refers to the selection of appropriate feature functions. 5 million features were used. For a given feature and vector set, the Viterbi algorithm mentioned can be used to determine the most probable tag sequence. In LCRF, a separate step is the selection of the appropriate parameter vector. For calculation of the maximum likelihood feature vector, numerical optimization is used.
N ) − k Θ2 2ν (6) where the superscript j denotes the training data index and ν denotes a penalty factor. At the optimum parameter value, the derivate of the objective function is equal to zero. The solution of the system of equations j j i j j f k (ti , ti−1 , wi ) − j j i t,t j f k (t, t , wi ) p(t, t , wi ) − Θ ν (7) yields the optimum parameter vector. The main shortcoming of this model is that it considers a sentence as a linear chain. Classic grammar models like TAG (tree adjoining grammar) are based on this approach.
In: 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI 2006), pp. 495–506, Budapest, ISBN 963 7154 54 X (2006) 15. : Rule approximation in metric spaces. In: Proceedings of 8th IEEE International Symposium on Applied Machine Intelligence and Informatics SAMI 2010, pp. 49–52, Herl’any, Slovakia (2010) 16. : Extending the fuzzy rule interpolation "FIVE" by fuzzy observation. advances in soft computing. In: Reusch, B. ) Computational Intelligence, Theory and Applications, pp.
Applied Information Science, Engineering and Technology: Selected Topics from the Field of Production Information Engineering and IT for Manufacturing: Theory and Practice by Csilla Csendes, Sándor Fegyverneki (auth.), Gabriella Bognár, Tibor Tóth (eds.)