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2018 | OriginalPaper | Chapter

A Comparative Analysis of Various Regularization Techniques to Solve Overfitting Problem in Artificial Neural Network

Authors : Shrikant Gupta, Rajat Gupta, Muneendra Ojha, K. P. Singh

Published in: Data Science and Analytics

Publisher: Springer Singapore

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Abstract

Neural networks having a large number of parameters are considered as very effective machine learning tool. But as the number of parameters becomes large, the network becomes slow to use and the problem of overfitting arises. Various ways to prevent overfitting of model are further discussed here and a comparative study has been done for the same. The effects of various regularization methods on the performance of neural net models are observed.

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Metadata
Title
A Comparative Analysis of Various Regularization Techniques to Solve Overfitting Problem in Artificial Neural Network
Authors
Shrikant Gupta
Rajat Gupta
Muneendra Ojha
K. P. Singh
Copyright Year
2018
Publisher
Springer Singapore
Book
Data Science and Analytics

Print ISBN: 978-981-10-8526-0

Electronic ISBN: 978-981-10-8527-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-8527-7_30

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