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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 - Undergoing reorganization
Department of Astronomy and Theoretical Physics - Undergoing reorganization
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}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Linearisation of feed-forward artificial networks to study input importance}},
  year         = {{2013}},
}