Download PDF by Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do,: Advanced Computational Methods for Knowledge Engineering

Download PDF by Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do,: Advanced Computational Methods for Knowledge Engineering

By Nguyen Van Thoai (auth.), Ngoc Thanh Nguyen, Tien van Do, Hoai An le Thi (eds.)

The publication contains 29 prolonged chapters that have been chosen and invited from the submissions to the 1st foreign convention on machine technological know-how, utilized arithmetic and Applications (ICCSAMA 2013) hung on 9-10 may possibly, 2013 in Warsaw, Poland. The booklet is equipped into 5 elements, that are: complicated Optimization tools and Their purposes, Queuing thought and purposes, Computational tools for wisdom Engineering, wisdom Engineering with Cloud and Grid Computing, and common sense dependent tools for selection Making and knowledge Mining, respectively. All chapters within the ebook speak about theoretical and useful concerns attached with computational tools and optimization equipment for wisdom engineering.

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Annals of Operations Research 133, 23–46 (2005) 7. html 8. : Portfolio optimization with linear and fixed transaction costs. Annals of Operations Research 157, 341–365 (2007) 9. : Portfolio selection. The Journal of Finance 7(1), 77–91 (1952) 10. c. programming: Theory, Algorithms and Applications. Acta Mathematica Vietnamica (dedicated to Professor Hoang Tuy on the occasion of his 70th birthday) 22(1), 289–355 (1997) 11. c. optimazation algorithm for solving the trust region subproblem. fr Abstract.

However, the resulting optimizations problems are very hard. Minimizing a function involving the zero norm is a challenge of the community of researchers in optimization and in machine learning. g. [12, 13, 17, 18], and references therein), in particular in machine learning for which they provide quite often a global solution and proved to be more robust and efficient than standard methods. Basing on the concave approximation [1] of the zero norm we reformulate the resulting optimization problems as DC programs and then design DCA schemes for solving them.

Net. Each of dataset is divided into two parts: the training set and the test set. These datasets are described in details in the table 1. 9. 1. For all methods, the most appropriate values of the parameter C are chosen by a five-fold cross-validation. 2 solver is used to solve linear or convex quadratic problems. , d for each feature ([3]). , Q wi Q 1 2 and set the ranking score cj as cj = Q i=1 wij . Then we remove the features j for which cj is smaller than a given threshold ρ. After removing features, for Feature Selection in MSVM 49 Table 1.

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