Top-down attention is modeled as decision making based on incomplete information. We consider decisions made in a sequential measurement situation where initially only an incomplete input feature vector is available, however, where we are given the possibility to acquire additional input values among the missing features. The procecure thus poses the question what to do next? We take an information theoretical approach implemented for generality in a generative mixture model. The framework allows us reduce the decision about what to measure next in a classification problem to the estimation of a few onedimensional integrals per missing feature. We demonstrate the viability of the framework on four well-known classification problems.
|Title of host publication||2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)|
|Publication status||Published - 2011|
|Event||2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain - Paris, France|
Duration: 11 Apr 2011 → 15 Apr 2011
|Conference||2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain|
|Period||11/04/2011 → 15/04/2011|