Main Reference PaperLossless Pruned Naive Bayes for Big Data Classifications, Big Data Research, 2018 [Python]
  • A Lossless Pruned Naive Bayes (LPNB) classification algorithm is proposed to big data applications with thousands of classes. It achieves significant speed-ups by drawing on Information Retrieval (IR) techniques for efficient posting list traversal and pruning.

Description
  • A Lossless Pruned Naive Bayes (LPNB) classification algorithm is proposed to big data applications with thousands of classes. It achieves significant speed-ups by drawing on Information Retrieval (IR) techniques for efficient posting list traversal and pruning.

  • To achieve the classification accuracy.

  • Low time complexity.

Aim & Objectives
  • To achieve the classification accuracy.

  • Low time complexity.

  • To extend the proposed idea into other linear machine learning models with applications of recommendation, search engines, and computational advertising.

Contribution
  • To extend the proposed idea into other linear machine learning models with applications of recommendation, search engines, and computational advertising.

  • 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.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

Leave Comment

Your email address will not be published. Required fields are marked *

clear formSubmit