How to Automate Neural Net Based Learning

Roland Linder, Siegfried J. Pöppl


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. In the present paper an approach is demonstrated fulfilling 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.

Original languageEnglish
Title of host publicationMLDM 2001: Machine Learning and Data Mining in Pattern Recognition
Number of pages11
PublisherSpringer Berlin Heidelberg
Publication date01.01.2001
ISBN (Print)978-3-540-42359-1
ISBN (Electronic)978-3-540-44596-8
Publication statusPublished - 01.01.2001
Event2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition
- Leipzig, Germany
Duration: 25.07.200127.07.2001
Conference number: 104830


Dive into the research topics of 'How to Automate Neural Net Based Learning'. Together they form a unique fingerprint.

Cite this