Main Reference Paper Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec, Information Sciences, 2018 [R]
  • A multi-co-training is proposed for improving the performance of document classification. For classification, the document is represented using following methods: term frequency–inverse document frequency (TF–IDF) based on the bag-of-words scheme, topic distribution based on latent Dirichlet allocation (LDA), and neural-network-based document embedding known as document to vector (Doc2Vec). Based on the three document representation methods, the classifier is trained.

Description
  • A multi-co-training is proposed for improving the performance of document classification. For classification, the document is represented using following methods: term frequency–inverse document frequency (TF–IDF) based on the bag-of-words scheme, topic distribution based on latent Dirichlet allocation (LDA), and neural-network-based document embedding known as document to vector (Doc2Vec). Based on the three document representation methods, the classifier is trained.

  • To improve the document classification.

  • Reducing computational complexity.

Aim & Objectives
  • To improve the document classification.

  • Reducing computational complexity.

  • A technique is contributed that improves the classification accuracy further.

Contribution
  • A technique is contributed that improves the classification accuracy further.

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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