Beyond prediction: Directions for probabilistic and relational learning
Publication Date
2008
Journal or Book Title
INDUCTIVE LOGIC PROGRAMMING
Abstract
Research over the past several decades in learning logical and probabilistic models has greatly increased the range of phenomena that machine learning can address. Recent work has extended these boundaries even further by unifying these two powerful learning frameworks. However, new frontiers await. Current techniques are capable of learning only a subset of the knowledge needed by practitioners in important domains, and further unification of probabilistic and logical learning offers a unique ability to produce the full range of knowledge needed in a wide range of applications.
DOI
https://doi.org/10.1007/978-3-540-78469-2_2
Pages
4-21
Volume
4894
Book Series Title
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Recommended Citation
Jensen, DD, "Beyond prediction: Directions for probabilistic and relational learning" (2008). INDUCTIVE LOGIC PROGRAMMING. 594.
https://doi.org/10.1007/978-3-540-78469-2_2