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

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

The booklet contains 29 prolonged chapters that have been chosen and invited from the submissions to the 1st overseas convention on machine technological know-how, utilized arithmetic and Applications (ICCSAMA 2013) hung on 9-10 may perhaps, 2013 in Warsaw, Poland. The booklet is geared up into 5 components, that are: complicated Optimization tools and Their functions, Queuing concept and functions, Computational equipment for wisdom Engineering, wisdom Engineering with Cloud and Grid Computing, and common sense established tools for selection Making and information Mining, respectively. All chapters within the publication speak about theoretical and sensible matters attached with computational equipment and optimization equipment for wisdom engineering.

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Spektrum 9, 153–159 (1987) 8. : On an outer approximation concept in global optimization. Optimization 20, 255–264 (1989) 9. : Concave minimization via conical partitions and polyhedral outer approximation. Mathematical Programming 50, 259–274 (1991) 10. : The cutting plane method for solving convex programs. SlAM Journal 8, 703–712 (1960) 11. : The Modified Cutting Angle Method for Global Minimization of Increasing Positively Homogeneous Functions over the Unit Simplex. Journal of Industrial and Management Optimization 5, 825–834 (2009) 12.

The Lung Cancer (LUN), Libras Movement (MOV), CNAE-9 (CNA), Hill-Valley (HIL), Spambase (SPA) and Internet Advertisement (ADV) are the datasets taken from UCI Machine Learning Repository. 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.

We are now developing a DCA scheme to solve the DC program (12). According to the generic DCA scheme, applying DCA on (12) amounts to computing, k k at each iteration k, a subgradient Y k = (w k , b , ξ ) of H1 at X k = (wk , bk , ξ k ) and then, solve the convex program: min {G1 (w, b, ξ) − Y k , X : X = (w, b, ξ) ∈ Ω}. , d, (14) and the convex program (13) is defined as ⎧ ⎫ Q d ⎨ ⎬ min G1 (X) − w kij wij : (w, b, ξ) ∈ Ω ⎩ ⎭ (15) i=1 j=1 ⎧ ⎨ ⇔ min C ⎩ Q n d max(αwij , −αwij ) − ξik + i=1 k=yi Q d i=1 j=1 wkij wij , (w, b, ξ) ∈Ω i=1 j=1 ⎫ ⎬ ⎭ .

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