Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
Most classification algorithms are "passive", in that they assign a class label to each instance based only on the description given, even if that description is incompl...
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an...
Document classification is a key task for many text mining applications. However, traditional text classification requires labeled data to construct reliable and accurate classifie...
A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduc...