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- W2391450451 abstract "In order to resolve the problem of actual large-scale data sets classification using SVM,this paper provides a method to improve the training speed through reducing data sets.The algorithm is divided into two steps.Firstly,it finds the samples that can represent a similar regional utilizing density clustering,then a set of support vectors will be gotten after using SVM to train reduced data sets.The second step is to find a new train data set assembled some support vectors and samples which belong to the regional represented by the support vector.The simulation shows that the algorithm proposed in this paper can improve classification speed while accuracy rate is acceptable." @default.
- W2391450451 created "2016-06-24" @default.
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- W2391450451 date "2011-01-01" @default.
- W2391450451 modified "2023-09-25" @default.
- W2391450451 title "Improved fast classifier based on SVM and density clustering" @default.
- W2391450451 hasPublicationYear "2011" @default.
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