ACMD: A Practical Tool for Automatic Neural Net Based Learning

Roland Linder, Siegfried J. Pöppl

Abstract

Although neural networks have many appealing properties, yet there is neither a systematic way how to set up the topology of a neural network nor how to determine its various learning parameters. Thus an expert is needed for fine tuning. If neural network applications should not be realisable only for publications but in real life, fine tuning must become unnecessary. We developed a tool called ACMD (Approximation and Classification of Medical Data) that is demonstrated to fulfil this demand. Moreover referring to six medical classification and approximation problems of the PROBEN1 benchmark collection this approach will be shown even to outperform fine tuned networks.

OriginalspracheEnglisch
TitelISMDA 2001: Medical Data Analysis
Seitenumfang6
Band2199
Herausgeber (Verlag)Springer Berlin Heidelberg
Erscheinungsdatum01.01.2001
Seiten168-173
ISBN (Print)978-3-540-42734-6
ISBN (elektronisch)978-3-540-45497-7
DOIs
PublikationsstatusVeröffentlicht - 01.01.2001
Veranstaltung2nd International Symposium on Medical Data Analysis
- Madrid, Spanien
Dauer: 08.10.200109.10.2001
Konferenznummer: 120889

Fingerprint

Untersuchen Sie die Forschungsthemen von „ACMD: A Practical Tool for Automatic Neural Net Based Learning“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren