Linearisation of feed-forward artificial networks to study input importance
(2013) FYTK01 20122Computational 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:
http://lup.lub.lu.se/student-papers/record/4015841
- author
- Larsson, Raoul LU
- supervisor
- organization
- course
- FYTK01 20122
- year
- 2013
- 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}}, }