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- W2371624298 abstract "The traditional spectrum algorithms had been limited in data classification problem.For its characteristics of problem,a novel algorithm based on common Gaussian process latent variable mode(CGP-LVM)and modified support vector machine(SVM)was proposed.Firstly,the probabilistic model of data manifold was established by the Gaussian process(GP),and GPLVM was gotten.The feature information was gotten by analyzing the data manifold.Secondly,the kernel function of SVM was modified by the multi-kernel iteration.Thereafter,the optimal data classifier was established.Finally,the data classification was achieved.Some data sets were selected as the experimental data,which consisted of FERET and UCI.A lot of experiments had been done.The results showed that the proposed method had not only agreat effect on accomplishing high-dimension data classification,but a great classification effect for the balanced data and the imbalanced data.The classification accuracy rate was higher than the traditional algorithms by 8%." @default.
- W2371624298 created "2016-06-24" @default.
- W2371624298 creator A5043041387 @default.
- W2371624298 date "2014-01-01" @default.
- W2371624298 modified "2023-09-27" @default.
- W2371624298 title "Novel data classification algorithm based on common GP-LVM and modified SVM" @default.
- W2371624298 hasPublicationYear "2014" @default.
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