Gillblad, Daniel and Holst, Anders and Steinert, Rebecca (2006) Fault-tolerant incremental diagnosis with limited historical data. [SICS Report]
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.
|Item Type:||SICS Report|
|Uncontrolled Keywords:||Incremental diagnosis, mixture models, Bayesian statistics, information theory|
|Deposited By:||Vicki Carleson|
|Deposited On:||29 Oct 2007|
|Last Modified:||18 Nov 2009 16:05|
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