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Fault-tolerant incremental diagnosis with limited historical data
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0001-8952-3542
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0001-8577-6745
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0002-5893-7774
2006 (English)Report (Other academic)
Abstract [en]

In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. All relevant information may not be available initially and must be acquired manually or at a cost. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. Here, we will describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system.

Place, publisher, year, edition, pages
Swedish Institute of Computer Science , 2006, 1. , p. 33
Series
SICS Technical Report, ISSN 1100-3154 ; 2006:17
Keywords [en]
Incremental diagnosis, mixture models, Bayesian statistics, information theory
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-22032OAI: oai:DiVA.org:ri-22032DiVA, id: diva2:1041574
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-09Bibliographically approved

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fulltext(617 kB)192 downloads
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Gillblad, DanielHolst, AndersSteinert, Rebecca

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf