The New Development in Support Vector Machine Algorithm Theory and Its Application

Liu, Taian; Wang, Yunjia; Wang, Yinlei; Liu, Wen-tong

doi:10.4304/jsw.6.2.273-280

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…[16] The support vector machine is a novel smallsample learning method, because it is based on the principle of structural risk minimization, rather than the traditional empirical risk minimization principle, it is superior to existing methods on many performances. [17] Support vector machines, being computationally powerful tools for supervised learning, are widely used in classification, clustering and regression problems. SVMs have been successfully applied to a variety of real-world problems like particle identification, face recognition, text categorization, bioinformatics, civil engineering and electrical engineering etc.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Making a Better Logistic Regression Model Using Ensemble Learning

Verma

2024

IJRASET

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Machine learning is a hot research topic in today's society and an important direction in the field of image classification research. Image classification is a supervised learning method used to classify images. There are many image classification algorithms that perform differently in different conditions. Paper focuses on improving the performance of image classification algorithms by using ensemble learning. Paper analyses the performance of logistic regression model and support vector machine model and then uses ensemble learning to increase the performance of logistic regression model. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of three algorithms when tested on same dataset, in same environment and on same system. The final results of the research prove that ensemble techniques give best results and this type of learning is effective.

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Making a Better Logistic Regression Model Using Ensemble Learning

Verma

2024

IJRASET

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“…LSSVM is an improvement of SVM, which replace the insensitive loss function of SVM with a quadratic loss function [10][11]. By constructing a quadratic loss function, the second classification optimization in SVM is transformed as a quadratic equation solution problem, in which way, decreases the complexity of computing and gains a better character on noise resistance and training speed.…”

Section: A Least Squares Support Vector Machine Principlementioning

confidence: 99%

Sensor Fault Diagnosis Based on Ensemble Empirical Mode Decomposition and Optimized Least Squares Support Vector Machine

Ding¹,

Wang²,

Shen³

et al. 2013

JCP

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A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis function (RBF) kernel function parameters and the regularization parameter of LSSVM are optimized by using chaotic particle swarm optimization (CPSO) algorithm. Finally, with the applying of proposed diagnosis method, the model of sensor fault diagnosis is built for identification and decision. The diagnostic results show that the proposed method can identify sensor fault effectively and accurately.

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“…It uses structural risk minimization principle instead of empirical risk minimization principle, which can seek the best compromise between model complexity and ability with limited sample information. SVM effectively resolves the problem of small sample size, high dimension and nonlinearity problem [1][2][3][4][5][6]. But for practical engineering application, the shortage that approximation algorithm and multifarious classification don't operate as well as two classes classification and less speed of training, will lead to a decreasing generalization of SVM [7].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

Ding¹,

Wang²,

Yang³

et al. 2013

JCP

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The classification accuracy of the least squares support vector machine (LSSVM) models strongly depends on proper setting of its parameters. An optimal selection approach of LSSVM parameters is put forward based on multi-swarm cooperative chaos particle swarm optimization (MCCPSO) algorithm. Chaos particle swarm optimization (CPSO) can improve the ability of local search optimization with good robust and adaptable. Multi-swarm cooperative particle swarm optimization (MCPSO) algorithm is masterslave heuristic method with a good global search. Then the MCCPSO-LSSVM diagnosis model is used to diagnosing analog circuit fault. Simulation results show that MCCPSO algorithm can jump out of local optimums with fast convergence and good stability. Results for analog circuit fault diagnosis show that the proposed method has strong robustness, and high accuracy.

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The New Development in Support Vector Machine Algorithm Theory and Its Application

Abstract: As to classification problem, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Base Show more

Cited by 6 publications

References 5 publications

Making a Better Logistic Regression Model Using Ensemble Learning

Making a Better Logistic Regression Model Using Ensemble Learning

Sensor Fault Diagnosis Based on Ensemble Empirical Mode Decomposition and Optimized Least Squares Support Vector Machine

Diagnosis Model Based on Least Squares Support Vector Machine Optimized by Multi-swarm Cooperative Chaos Particle Swarm Optimization and Its Application

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