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Linearisation of feed-forward artificial networks to study input importance

Larsson, Raoul LU (2013) FYTK01 20122
Computational Biology and Biological Physics
Department of Astronomy and Theoretical Physics
Abstract (Swedish)
In the use of articial neural networks (ANN) for real-life applications
there is a need for determining the importance of each input variable for
the decisions of the articial neural network. By comparing more complex
ANN:s with the simple perceptron, a basic method to determine some form
of importance is found. This is tested on three dierent datasets of which
one is based on a real life dataset of acute coronary syndrome (ACS). The
results from these tests indicate that the method works.
Please use this url to cite or link to this publication:
author
Larsson, Raoul LU
supervisor
organization
course
FYTK01 20122
year
type
M2 - Bachelor Degree
subject
keywords
Artificial Neural Network, Multiple Layer Perceptron, Ensemble, Simple Perceptron, Acute Coronary Syndrome
language
English
id
4015841
date added to LUP
2014-03-20 16:00:51
date last changed
2017-10-06 16:41:10
@misc{4015841,
  abstract     = {In the use of articial neural networks (ANN) for real-life applications
there is a need for determining the importance of each input variable for
the decisions of the articial neural network. By comparing more complex
ANN:s with the simple perceptron, a basic method to determine some form
of importance is found. This is tested on three dierent datasets of which
one is based on a real life dataset of acute coronary syndrome (ACS). The
results from these tests indicate that the method works.},
  author       = {Larsson, Raoul},
  keyword      = {Artificial Neural Network,Multiple Layer Perceptron,Ensemble,Simple Perceptron,Acute Coronary Syndrome},
  language     = {eng},
  note         = {Student Paper},
  title        = {Linearisation of feed-forward artificial networks to study input importance},
  year         = {2013},
}