Fault-tolerant incremental diagnosis with limited historical data
Number of Authors: 3
2006 (English)Report (Refereed)
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. , 33 p.
SICS Technical Report, ISSN 1100-3154 ; 2006:17
Incremental diagnosis, mixture models, Bayesian statistics, information theory
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-22032OAI: oai:DiVA.org:ri-22032DiVA: diva2:1041574