Rare Fault Detection by Possibilistic Reasoning

dc.contributor.author	Goser, Karl	de
dc.contributor.author	Kanstein, Andreas	de
dc.contributor.author	Thomas, Marc	de
dc.date.accessioned	2004-12-07T08:19:45Z
dc.date.available	2004-12-07T08:19:45Z
dc.date.created	1998	de
dc.date.issued	1998-11-08	de
dc.description.abstract	Kernel based neural networks with probabilistic reasoning are suitable for many practical applications. But influence of data set sizes let the probabilistic approach fail in case of small data amounts. Possibilistic reasoning avoids this drawback because it is independent of class size. The fundamentals of possibilistic reasoning are derived from a probability/possibility consistency principle that gives regard to relations. It is demonstrated that the concept of possibilistic reasoning is advantageous for the problem of rare fault detection, which is a property desired for semiconductor manufacturing quality control.	en
dc.format.extent	121988 bytes
dc.format.extent	99829 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5354
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15284
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 24	de
dc.subject.ddc	004	de
dc.title	Rare Fault Detection by Possibilistic Reasoning	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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