Paper Info
Title
Bayesian Data Fusion And Credit Assignment In Vision And Fmri Data Analysis
Abstract
One of the most important challenges in understanding expert perception is in determining what information in a complex scene is most valuable (reliable) for a particular task. and how experts learn to exploit it. For the task of parameter estimation given multiple independent sources of data, Bayesian data fusion provides a solution to this problem that involves promoting data to a common parameter space and combining cues weighted by their reliabilities. However, for classification tasks this approach needs to be modified to find the information that most reliably distinguishes between the categories. In this paper we discuss solutions to the problem of determining the task-dependent reliability of data sources both objectively for a Bayesian decision agent, and in terms of the reliability assigned by a human observer from the performance of the observer. Modeling observers as Bayesian decision agents, solutions can be construed as a process of assigning credit to data sources based on their contribution to task performance. Applications of this approach to human perceptual data and the analysis of fMRI data will be presented.
Year
DOI
Venue
2003
10.1117/12.487986
COMPUTATIONAL IMAGING
Keywords
Field
DocType
Bayesian data fusion, feature selection, human perception
Data mining,Feature selection,Computer science,Sensor fusion,Exploit,Artificial intelligence,Estimation theory,Observer (quantum physics),Perception,Machine learning,Visual perception,Bayesian probability
Conference
Volume
ISSN
Citations 
5016
0277-786X
2
PageRank 
References 
Authors
0.43
0
1
Name
Order
Citations
PageRank
Paul R. Schrater114122.71