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- W2369453092 abstract "At present,the multi-objective output SVM regression uses multi-threshold strategy.However,its prediction result and computational complexity is not satisfactory.In addition,the parameter and feature selection generally uses cross-validation as the evaluation criteria.Because of the imbalance data,the statistical methods used to evaluate the cross-validation error cannot always get the optimal effect.In this paper,an algorithm of the multi-objective output SVM regression is proposed using the same thresholds for the multi-objective,and the mean square error for all objectives are treated homogeneously.Finally,for large super-computing problem,the parallel computing algorithm for network computer is proposed.Experiments are carried out on the same SVM model,for the parameter optimization by using genetic algorithms,particle swarm optimization and own Breadth-first search algorithm separately,and for the feature selection by using the modified sequential minimal algorithm.Results show that parameter optimization using Breadth-first search algorithm,cross-validation with homogenization of the mean square error for the purpose of evaluation criteria is relatively good.The model is an effective method for Multi-objective Problems." @default.
- W2369453092 created "2016-06-24" @default.
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- W2369453092 date "2010-01-01" @default.
- W2369453092 modified "2023-09-23" @default.
- W2369453092 title "Applications of Support Vector Machine Optimization in Multi-objective Problems" @default.
- W2369453092 hasPublicationYear "2010" @default.
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