Refereed full papers (journals, book chapters, international conferences)

1997

Machine learning cannot occur without some means to represent the learned knowledge. Researchers have long recognized the influence of representational choices, and the major paradigms in machine learning are organized not around induction algorithms or performance elements as much as around representational classes. Major examples include logical representations, which encode knowledge as rule sets or as univariate decision trees, neural networks, which instead use nodes connected by weighted links, and instance-based approaches, which store specific training cases in memory. In the late 1980s, work on probabilistic representations also started to appear in the machine learning literature. This representational framework had a number of attractions, including a clean probabilistic semantics and the ability to explicitly describe degrees of certainty. This general approach attracted only a moderate amount of attention until recent years, when progress on Bayesian belief networks led to enough activity in the area to justify this special issue on the topic of probabilistic learning ...