One of the key issues in designing appropriate and effective learning environments is understanding how learners advance and what factors contribute to their progress. This holds...
This paper describes a novel intellectual structure for the subject space of material designed for selective autodidactic learning in a large knowledge base. This structure is base...
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...
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw o...
In the inductive inference framework of learning in the limit, a variation of the bounded example memory (Bem) language learning model is considered. Intuitively, the new model con...