An Efficient Support Vector Machine Learning Method with Second-Order Cone Programming for Large-Scale Problems

2005 ◽  
Vol 23 (3) ◽  
pp. 219-239 ◽  
Author(s):  
Rameswar Debnath ◽  
Masakazu Muramatsu ◽  
Haruhisa Takahashi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Large Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Large Scale Problems ◽  
Second Order Cone

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

A novel second-order cone programming support vector machine model for binary data classification

2020 ◽  
Vol 39 (3) ◽  
pp. 4505-4513
Author(s):  
Guishan Dong ◽  
Xuewen Mu
Keyword(s):  
Support Vector Machine ◽  
Second Order ◽  
Misclassification Rate ◽  
Support Vector ◽  
Programming Formulation ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Second Order Cone ◽  
Soft Margin ◽  
Programming Support

The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l2-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.

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