Kernel functions can be viewed as a non-linear transformation that increases the separability of the input data by mapping them to a new high dimensional space. The incorporation ...
We present a method for unsupervised boundary classijication by producing and analyzing intensity profiles. Each profile is created by sampling an ellipsoidal neighborhood of voxe...
We introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (IAA). The proposed method is referred to as the missing-data IAA (M...
In this paper, we derive lower and upper bounds for the probability of error for a linear classifier, where the random vectors representing the underlying classes obey the multivar...
We consider the problem and issues of classifier fusion and discuss how they should be reflected in the fusion system architecture. We adopt the Bayesian viewpoint and show how thi...