Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
We proposed a new approach to compare profiles when the correlations among attributes can be represented as a tree. To account for these correlations, the profile is extended with...
Output coding is a general method for solving multiclass problems by reducing them to multiple binary classification problems. Previous research on output coding has employed, alm...
In this paper we present a new approach to classifying radiographs, which is the first important task of the IRMA system. Given an image, we compute posterior probabilities for ea...
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This pa...