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

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

ISBN-10: 3319002929

ISBN-13: 9783319002927

The ebook includes 29 prolonged chapters which were 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 possibly, 2013 in Warsaw, Poland. The ebook is equipped into 5 elements, that are: complicated Optimization equipment and Their purposes, Queuing thought and purposes, Computational tools for wisdom Engineering, wisdom Engineering with Cloud and Grid Computing, and good judgment dependent tools for determination Making and information Mining, respectively. All chapters within the booklet speak about theoretical and useful concerns hooked up with computational tools and optimization equipment for wisdom engineering.

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A. ): ICCSAMA 2013, SCI 479, pp. 41–52. A. C. , n}. , Q. A (multi-class) classifier is a function f : X → Y that maps an element x to a category y ∈ Y. , Q} and let b be a vector in IRQ . , . is the scalar product in IRd space. , Q}, that separate the training dataset in the best way. So, the MSVM model [22] is defined by: Q n min C w,b,ξ ξik + i=1 k=yi wk 2 2 (2) k=1 subject to ⎧ ⎨ wyi − wk , xi + byi − bk ≥ 1 − ξik , (1 ≤ i ≤ n), (1 ≤ k = yi ≤ Q) (1 ≤ i ≤ n), (1 ≤ k = yi ≤ Q), Ω : ξik ≥ 0, ⎩ n where ξik ∈ IRn∗Q are slack variables.

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. The description of the datasets Dataset #train #test #feature #class LUN 16 16 56 3 MOV 255 105 90 15 CNA 500 580 856 9 HIL 606 606 100 2 SPA 2301 2300 57 2 ADV 1000 2279 1558 2 ADN 1064 2122 60 3 Table 2. 00 computing the accuracy of classification, we apply again l2 -MSVM (2) on the new training datasets and calculate the classification’s accuracy on the new test sets.

I) If γk = μk , then stop, xk is a global optimal solution and γk is the optimal value of Problem (7)). (ii) If γk > μk , then divide S k into rk subsimplices S1k , . . 1). 26 N. Van Thoai (iii) For each i = 1, . . , rk , check the set Sik ∩ D. If Sik ∩ D = ∅, then set μ(Sik ) = +∞. Otherwise, determine a finite set Q(Sik ) ⊂ Sik ∩ D. If there is a point y ∈ Q(Sik ) with f (y) = 0, then stop, y is a global optimal solution of Problem (7). Otherwise, compute ν(Sik ) by solving Problem (12) according to Sik , and set μ(Sik ) = max{μ(S k ), ν(Sik )}.

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