Experimental assessment of the performance of classification algorithms is an important aspect of their development and application on real-world problems. To facilitate this analy...
An efficient framework is proposed for the fast recovery of Bayesian network classifier. A novel algorithm, called Iterative Parent-Child learningBayesian Network Classifier (IPC-...
Constructivism has gained popularity recently, but it is not a completely new learning paradigm. Much of the work within Information Systems Science (IS) and especially within ele...
Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previous...
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...