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Necktie - A Profound LEARNING FEEDFORWARD NEURAL System FOR Opinion Investigation

EasyChair Preprint no. 2565

8 pagesDate: February 5, 2020


The most effective method to demonstrate and encode the semantics of human-composed content and choose the kind of neural system to process it are not settled issues in conclusion investigation. Exactness and transferability are basic issues in AI as a rule. These properties are firmly identified with the misfortune gauges for the prepared model. I present a computationally-effective and precise feedforward neural system for slant expectation equipped for keeping up low misfortunes. At the point when combined with a viable semantics model of the content, it furnishes exceptionally exact models with low misfortunes. Trial results on delegate benchmark datasets and correlations with different strategies show the upsides of the new methodology.

Keyphrases: Artificial Intelligence, deep learning, machine learning, Neural Network.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Alok Kumar Yadav},
  title = {Necktie - A Profound LEARNING FEEDFORWARD NEURAL System FOR Opinion Investigation},
  howpublished = {EasyChair Preprint no. 2565},

  year = {EasyChair, 2020}}
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