Web Spam Classification Using Supervised Artificial Neural Network Algorithms
A. Chandra, M. Suaib, and R. Beg. Advanced Computational Intelligence: An International Journal (ACII), 2 (1):
10(January 2015)
Abstract
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation
learning, and Levenberg-Marquardt algorithm.
%0 Journal Article
%1 chandra2015classification
%A Chandra, Ashish
%A Suaib, Mohammad
%A Beg, Rizwan
%D 2015
%J Advanced Computational Intelligence: An International Journal (ACII)
%K classification spam web
%N 1
%P 10
%T Web Spam Classification Using Supervised Artificial Neural Network Algorithms
%U http://airccse.org/journal/acii/papers/2115acii02.pdf
%V 2
%X Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation
learning, and Levenberg-Marquardt algorithm.
@article{chandra2015classification,
abstract = {Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation
learning, and Levenberg-Marquardt algorithm. },
added-at = {2018-06-12T21:54:27.000+0200},
author = {Chandra, Ashish and Suaib, Mohammad and Beg, Rizwan},
biburl = {https://www.bibsonomy.org/bibtex/2f9ab3d13cd0f0b1ba8d11e1832e30ab6/nosebrain},
interhash = {8e0a4abfb009e711649cc44f8c972f74},
intrahash = {f9ab3d13cd0f0b1ba8d11e1832e30ab6},
issn = {2454 - 3934},
journal = { Advanced Computational Intelligence: An International Journal (ACII)},
keywords = {classification spam web},
language = {English},
month = {January},
number = 1,
pages = 10,
timestamp = {2018-06-12T21:54:27.000+0200},
title = {Web Spam Classification Using Supervised Artificial Neural Network Algorithms},
url = {http://airccse.org/journal/acii/papers/2115acii02.pdf},
volume = 2,
year = 2015
}