umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
trainGPP - Users' manual
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
2004 (Engelska)Rapport (Övrigt vetenskapligt)
Abstract [en]

The generalised preprocessing perceptron (GPP) is a model that has the advantage of few parameters and still good discriminance ability. Still the GPP is very general and the user might need some support when building and training the model. The toolbox trainGPP , written by the author in MATLAB, is described. The toolbox can be provided by the author and is used to create different types of GPP:s and to train them.

In this paper a tutorial of the different parts of trainGPP is given. It is assumed that the reader is familiar with the GPP and the theory behind it. However, an appendix is included where a presentation of the generalised preprocessing perceptron is given together with descriptions of different kind of GPPmodels.

Ort, förlag, år, upplaga, sidor
Dept. of Computing Science, Umeå University , 2004. , s. 27
Serie
Report / UMINF, ISSN 0348-0542 ; 01
Identifikatorer
URN: urn:nbn:se:umu:diva-8401OAI: oai:DiVA.org:umu-8401DiVA, id: diva2:148072
Tillgänglig från: 2008-01-21 Skapad: 2008-01-21 Senast uppdaterad: 2018-06-09Bibliografiskt granskad
Ingår i avhandling
1. Preprocessing perceptrons
Öppna denna publikation i ny flik eller fönster >>Preprocessing perceptrons
2004 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi-layer perceptron.

The research in this thesis has been conducted within the fields of medical informatics and intelligent computing. The original idea of the production line as a tool for a domain expert to extract information, build decision support systems and integrate them in the existing system is described. In the introductory part of the thesis, an introduction to feed-forward neural networks and fuzzy logic is given as a background to work with the preprocessing perceptron. Input to a decision support system is crucial and it is described how to gather a data set, decide how many and what kind of inputs to use. Outliers, errors and missing data are covered as well as normalising of the input. Training is done in a backpropagation-like manner where the division of the data set into a training and a test set can be done in several different ways just as the training itself can have variations. Three major groups of methods to estimate the discriminance effect of the preprocessing perceptron are described and a discussion of the trade-off between complexity and approximation strength are included.

Five papers are presented in this thesis. Case studies are shown where the preprocessing perceptron is compared to multi-layer perceptrons, statistical approaches and other mathematical models. The model is extended to a generalised preprocessing perceptron and the performance of this new model is compared to the traditional feed-forward neural networks. Results concerning the preprocessing layer and its connection to multivariate decision limits are included. The well-known ROC curve is described and introduced fully into the field of computer science as well as the improved curve, the QROC curve. Finally a tutorial to the program trainGPP is presented. It describes how to work with the preprocessing perceptron from the moment when a data file is provided to the moment when a new decision support system is built.

Ort, förlag, år, upplaga, sidor
Umeå: Datavetenskap, Umeå universitet, 2004. s. 158
Serie
Report / UMINF, ISSN 0348-0542 ; 04.10
Nyckelord
Datalogi, Preprocessing perceptron, Production line, Neural networks, Backpropagation, Fuzzy logic, ROC and QROC curves, Multivariate decision limits, Datalogi
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
administrativ databehandling
Identifikatorer
urn:nbn:se:umu:diva-234 (URN)91-7305-645-6 (ISBN)
Disputation
2004-05-14, MA121, MIT-huset, Umeå Universitet, Umeå, 13:15
Opponent
Handledare
Tillgänglig från: 2004-04-15 Skapad: 2004-04-15 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

UMINF reports by Lena Kallin Westin

Personposter BETA

Kallin Westin, Lena

Sök vidare i DiVA

Av författaren/redaktören
Kallin Westin, Lena
Av organisationen
Institutionen för datavetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 161 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf