>I'm interested in methods/heuristics for selecting a group
>of test variables that have a large/largest value of information.
>Most approaches are myopic (i.e., select the variable with maximal
>VOI, test its value, select the next one, etc.). I found only two
>relevant references: Heckerman et al. 1993 discuss approximate
>non-myopic VOI computation, and Madigan and Russell 1995
>discuss test selection strategies. Both say that it is a key
>capability of an expect system, but surprisintly I could not
>find any work beyond this. In particular, I'm happy with the
>Heckerman et al. model in which there is a single binary decision
>and a single binary chance node affecting the value function.
>
>Can anyone recommend additional useful references?
>
>Thanks,
>
>Ronen
Some work has been done on heuristics for non myopic hypothesis driven data
request in the context of parallel feature extraction in real-time video
analysis. This work may be of relevance. The work will be published at the
2002 International Conference on Parallel and Distributed Processing
Techniques and Applications (PDPTA'02), Las Vegas, USA, June 2002. The
abstract of the paper can be found below. If you are interested I can e-mail
you the paper.
Ole-Christoffer Granmo
*************************************************************************
Real-time Hypothesis Driven Feature Extraction on Parallel Processing
Architectures
Ole-Christoffer Granmo and Finn Verner Jensen
Feature extraction in content-based indexing of media streams is often
computational intensive. Typically, a parallel processing architecture is
necessary for real-time performance when extracting features brute force. On
the other hand, Bayesian network based systems for hypothesis driven feature
extraction, which selectively extract relevant features one-by-one, have in
some cases achieved real-time performance on single processing element
architectures. In this paper we propose a novel technique which combines the
above two approaches. Features are selectively extracted in parallizable
sets, rather than one-by-one. Thereby, the advantages of parallel feature
extraction can be combined with the advantages of hypothesis driven feature
extraction. The technique is based on a sequential backward feature set
search and a correlation based feature set evaluation function. In order to
reduce the problem of higher-order feature-content/feature-feature
correlation, causally complexly interacting features are identified through
Bayesian network d-separation analysis and combined into joint features.
When used on a moderately complex object-tracking case, the technique is
able to select parallelizable feature sets real-time in a goal oriented
fashion, even when some features are pairwise highly correlated and causally
complexly interacting.
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