scholarly journals On Fuzzy Bi-Level Multi-Objective Large Scale Integer Quadratic Programming Problem

2017 ◽  
Vol 159 (2) ◽  
pp. 28-33
Author(s):  
O. E. ◽  
E. Fathy ◽  
A. A.
Keyword(s):  
Quadratic Programming ◽  
Programming Problem ◽  
Large Scale ◽  
Quadratic Programming Problem ◽  
Multi Objective ◽  
Integer Quadratic Programming

Large-Scale Data Classification Based on Ball Vector Machine

2013 ◽  
Vol 312 ◽  
pp. 771-776
Author(s):  
Min Juan Zheng ◽  
Guo Jian Cheng ◽  
Fei Zhao
Keyword(s):  
Quadratic Programming ◽  
Programming Problem ◽  
Time Complexity ◽  
Large Scale ◽  
Space Complexity ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Data Sets ◽  
Standard Support Vector Machine ◽  
Large Scale Data

The quadratic programming problem in the standard support vector machine (SVM) algorithm has high time complexity and space complexity in solving the large-scale problems which becomes a bottleneck in the SVM applications. Ball Vector Machine (BVM) converts the quadratic programming problem of the traditional SVM into the minimum enclosed ball problem (MEB). It can indirectly get the solution of quadratic programming through solving the MEB problem which significantly reduces the time complexity and space complexity. The experiments show that when handling five large-scale and high-dimensional data sets, the BVM and standard SVM have a considerable accuracy, but the BVM has higher speed and less requirement space than standard SVM.

scholarly journals A Multi-Level Multi-Objective Quadratic Programming Problem with Fuzzy Parameters on Objective Functions

2016 ◽  
Vol 15 (5) ◽  
pp. 6738-6748 ◽  
Author(s):  
Usama Emam
Keyword(s):  
Quadratic Programming ◽  
Programming Problem ◽  
Decision Model ◽  
Fuzzy Numbers ◽  
Quadratic Programming Problem ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Level ◽  
Fuzzy Parameters

This paper proposes an algorithm to solve multi-level multi-objective quadratic programming problem with fuzzy parameters in the objective functions, This algorithm uses the tolerance membership function concepts and multi-objective optimization at each level to develop a fuzzy Max-Min decision model for generating satisfactory solution after applying linear ranking method on trapezoidal fuzzy numbers in the objective functions, An illustrative example is included to explain the results.

scholarly journals Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

2020 ◽  
Vol 10 (19) ◽  
pp. 6979
Author(s):  
Minho Ryu ◽  
Kichun Lee
Keyword(s):  
Support Vector Machines ◽  
Quadratic Programming ◽  
Decision Trees ◽  
Programming Problem ◽  
Large Scale ◽  
Classification Performance ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.

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