This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
Feature interactions in the original sense of the term (i.e. within a telecommunications domain), have now been the subject of significant research activity for over ten years. Thi...
Lynne Blair, Gordon S. Blair, Jianxiong Pang, Chri...
Multi-domain application environments where distributed domains interoperate with each other are becoming a reality in Internet-based enterprise applications. The secure interoper...
This paper describes DIDO, a system we have developed to carry out exploratory learning of unfamiliar domains without assistance from an external teacher. The program incorporates...