Deep Neural Network Regularization for Feature Selection in Learning-to-Rank

Deep Neural Network Regularization for Feature Selection in Learning-to-Rank

Learning-to-rank is an emerging area of research for a wide range of applications. Many algorithms are devised to tackle the problem of learning-to-rank. However, very few existing algorithms deal with deep learning. Previous research depicts that deep learning makes significant improvements in a va...

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Bibliographic Details
Main Authors: Ashwini Rahangdale, Shital Raut
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Deep neural network
feature selection
information retrieval
learning-to-rank
regularization
Online Access:https://ieeexplore.ieee.org/document/8700495/

Internet

https://ieeexplore.ieee.org/document/8700495/

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