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dc.contributor.authorWang, C.
dc.contributor.authorDong, Y
dc.contributor.authorXia, Y.
dc.contributor.authorLi, G.
dc.contributor.authorMartínez, O.S
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.date2020
dc.date.accessioned2022-02-17T13:08:05Z
dc.date.available2022-02-17T13:08:05Z
dc.identifier.issn08247935
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12463
dc.description.abstractFor the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to the employment and entrepreneurship management of college students. Based on deep learning technology, the deep neural network is constructed based on SVR and restrictive Boltzmann machine, namely, SVR-DBN, including theoretical derivation of model architecture, design and selection of model training algorithms, and the modeling steps and flow charts are given, and finally applied to the influence factor analysis. The multiangle comparison proves that the proposed depth model has excellent feature extraction ability and regression prediction. The results show that the algorithm has higher accuracy and has a 26% improvement over traditional algorithms. The research is of great significance to the improvement of the efficiency of employment and entrepreneurship management and the application of support vector machine algorithms. © 2020 Wiley Periodicals LLC.es_ES
dc.language.isoenges_ES
dc.publisherBlackwell Publishing Inc.es_ES
dc.relation.ispartofseries;online
dc.relation.urihttps://onlinelibrary.wiley.com/doi/10.1111/coin.12430es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectcollege studentses_ES
dc.subjectemployment and entrepreneurshipes_ES
dc.subjectmanagement mechanismes_ES
dc.subjectsupport vector machine algorithmes_ES
dc.subjectScopuses_ES
dc.subjectJCRes_ES
dc.titleManagement and entrepreneurship management mechanism of college students based on support vector machine algorithmes_ES
dc.typearticlees_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1111/coin.12430


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