Active training of backpropagation neural networks using the learning by experimentation methodology

Fu-Ren Lin and Michael Shaw

Annals of Operations Research, 1997, vol. 75, issue 0, 105-122

Abstract: This paper proposes the Learning by Experimentation Methodology (LEM) to facilitate the active training of neural networks. In an active learning paradigm, a learning mechanism can actively interact with its environment to acquire new knowledge and revise itself. The learning by experimentation is an active learning strategy. Experiments are conducted to form hypotheses, and the evaluation of those hypotheses feeds back to the learning mechanism to revise knowledge. We use a backpropagation neural network as the learning mechanism. We also adopt a weight space analysis method and a heuristic to select salient attributes to perform new experiments in order to revise the network. Finally, we illustrate performance by solving the sonar signal classification problem. Copyright Kluwer Academic Publishers 1997

Keywords: neural networks; Learning by Experimentation Methodology; active learning (search for similar items in EconPapers)
Date: 1997
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1018999110972 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:75:y:1997:i:0:p:105-122:10.1023/a:1018999110972

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1023/A:1018999110972

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().