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- W2390700952 abstract "With software development becoming an essential investment for many organizations recently,both the software industry and academic communities have become more and more concerned about reliable and accurate estimates of software development effort.This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm(GA)to the support vector regression(SVR)model with RBF kernels.The genetic algorithm(GA)method is adapted to optimize the parameters(Cγ,e)in support vector regression.The experiments are carried out using three datasets of software projects from IBM DP,Kemerer and Hallmark.The effectiveness of the GA-SVR model is compared with other techniques such as linear regression in terms of the error measure which is mean magnitude relative error(MMRE)and Pred(0.25).The results have shown that GA-SVR model has superior learning accuracy and generalization performance and significantly outperforms linear regression in three datasets." @default.
- W2390700952 created "2016-06-24" @default.
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- W2390700952 date "2011-01-01" @default.
- W2390700952 modified "2023-09-23" @default.
- W2390700952 title "Hybrid Support Vector Regression Model with Optimized Parameter for Software Effort Estimation" @default.
- W2390700952 hasPublicationYear "2011" @default.
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