Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
We investigate why discretization is effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naiveBay...
In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by manyresearchers. Our analysis assumes a con...
A global parametric shape model (boundary) of the object is optimized according to evidence accumulated from local features and the prior probability of the model parameters learn...
Attribute interactions are the irreducible dependencies between attributes. Interactions underlie feature relevance and selection, the structure of joint probability and classific...