Building A Bi-objective Quadratic Programming Model for the Support Vector Machine

2020 ◽  
Author(s):  
Mohammed Zakaria Moustafa ◽  
Mohammed Rizk Mohammed ◽  
Hatem Awad Khater
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Support Vector

A MULTI-CLASS SUPPORT VECTOR MACHINE: THEORY AND MODEL

2013 ◽  
Vol 12 (06) ◽  
pp. 1175-1199 ◽  
Author(s):  
MINGHE SUN
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Inner Product ◽  
High Dimensional ◽  
Support Vector ◽  
Test Problems ◽  
Discriminant Functions ◽  
Input Space ◽  
Feature Spaces

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional feature spaces using inner product kernels, and its performance is tested. The M-SVM is formulated as a quadratic programming model. Its dual, also a quadratic programming model, is very elegant and is easier to solve than the primal. The discriminant functions can be directly constructed from the dual solution. By using inner product kernels, the M-SVM can be built and nonlinear discriminant functions can be constructed in high dimensional feature spaces without carrying out the mappings from the input space to the feature spaces. The size of the dual, measured by the number of variables and constraints, is independent of the dimension of the input space and stays the same whether the M-SVM is built in the input space or in a feature space. Compared to other models published in the literature, this M-SVM is equally or more effective. An example is presented to demonstrate the dual formulation and solution in feature spaces. Very good results were obtained on benchmark test problems from the literature.

scholarly journals A mixed quadratic programming model for a robust support vector machine

2021 ◽  
Vol 8 (1) ◽  
pp. 27-36
Author(s):  
Raquel Serna-Diaz ◽  
Raimundo Santos Leite ◽  
Paulo J. S. Silva
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Support Vector

scholarly journals Dual possibilistic regression models of support vector machines and application in power load forecasting

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092163
Author(s):  
Xianfei Yang ◽  
Xiang Yu ◽  
Hui Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Regression Models ◽  
Load Forecasting ◽  
Interval Data ◽  
Support Vector ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

A Comparison of Quadratic Programming Solvers in Support Vector Machines Training

2012 ◽  
Author(s):  
N. M. Zaki ◽  
S. Deris ◽  
K. K. Chin
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Key Words ◽  
Execution Time ◽  
Practical Method ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Penyelesaian atur cara kuadratik yang sangat besar diperlukan untuk melatih Support Vector Machine. Tiga cara penyelesaian atur cara kuadratik yang berbeza telah digunakan untuk melaksanakan latihan Support Vector Machine bagi mengkaji keberkesanannya ke atas Support Vector Machine. Prestasi bagi kesemua penyelesaian telah dikaji dan dianalisis dari segi masa pelaksanaan dan kualiti penyelesaian. Kaedah praktikal untuk mengurangkan masa latihan tersebut telah dikaji sepenuhnya. Kata kunci: Support vector machines, atur cara kuadratik Training a Support Vector Machine requires the solution of a very large quadratic programming problem. In order to study the influence of a particular quadratic programming solver on the Support Vector Machine, three different quadratic programming solvers are used to perform the Support Vector Machine training. The performance of these solvers in term of execution time and quality of the solutions are analyzed and compared. A practical method to reduce the training time is investigated. Key words: Support vector machines, quadratic programming

scholarly journals A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromise Solution for Measuring the Degree of Infection With Corona Virus Disease (Covid-19)

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Mohammed Zakaria Moustafa ◽  
Hassan Mahmoud Elragal ◽  
Mohammed Rizk Mohammed ◽  
Hatem Awad Khater ◽  
Hager Ali Yahia
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Virus Disease ◽  
Fuzzy Model ◽  
Efficient Solutions ◽  
Support Vector ◽  
Compromise Solution ◽  
Weighting Method ◽  
Quadratic Programming Problems ◽  
Objective Model

A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).

SUPPORT VECTOR MACHINE BASED ON RANDOM SET SAMPLES

2013 ◽  
Vol 21 (supp01) ◽  
pp. 101-112
Author(s):  
CHAO WANG ◽  
MINGHU HA ◽  
JIQIANG CHEN ◽  
HONGJIE XING
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Set Theory ◽  
Real World ◽  
Convex Quadratic Programming ◽  
Experimental Results ◽  
Learning Problems ◽  
Support Vector ◽  
Random Set ◽  
Random Set Theory

In order to deal with learning problems of random set samples encountered in real-world, according to random set theory and convex quadratic programming, a new support vector machine based on random set samples is constructed. Experimental results show that the new support vector machine is feasible and effective.

scholarly journals A Fuzzy BI-Objective Model for SVM with an Interactive Procedure to Identify the Best Compromise Solution

2020 ◽  
Author(s):  
Hager Ali Yahia ◽  
Mohammed Zakaria Moustafa ◽  
Mohammed Rizk Mohammed ◽  
Hatem Awad Khater
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Fuzzy Model ◽  
Efficient Solutions ◽  
Support Vector ◽  
Compromise Solution ◽  
Weighting Method ◽  
Quadratic Programming Problems ◽  
Objective Model ◽  
Fuzzy Parameters

A support vector machine (SVM) learns the decision surface from two different classes of the input points. In many applications, there are misclassifications in some of the input points and each is not fully assigned to one of these two classes. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. An important contribution will be added for the proposed fuzzy bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.

scholarly journals GPUSVM: a comprehensive CUDA based support vector machine package

2011 ◽  
Vol 1 (4) ◽  
Author(s):  
Qi Li ◽  
Raied Salman ◽  
Erik Test ◽  
Robert Strack ◽  
Vojislav Kecman
Keyword(s):  
Support Vector Machine ◽  
Programming Model ◽  
Support Vector ◽  
Processing Unit ◽  
Training Tool ◽  
Background Theory ◽  
Device Architecture ◽  
Fast Training ◽  
Cuda Programming ◽  
User Friendly

AbstractGPUSVM (Graphic Processing Unit Support Vector Machine) is a Computing Unified Device Architecture (CUDA) based Support Vector Machine (SVM) package. It is designed to offer an end-user a fully functional and user friendly SVM tool which utilizes the power of GPUs. The core package includes an efficient cross validation tool, a fast training tool and a predicting tool. In this article, we first introduce the background theory of how we build our parallel SVM solver using CUDA programming model. Then we compare our GPUSVM package with the popular state of the art Libsvm package on several well known datasets. The preliminary results have shown one to two orders of magnitude speed improvement in both training and predicting phases compared to Libsvm using our Tesla server.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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