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ILP
2007
Springer

Beyond Prediction: Directions for Probabilistic and Relational Learning

12 years 3 months ago
Beyond Prediction: Directions for Probabilistic and Relational Learning
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.
David D. Jensen
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ILP
Authors David D. Jensen
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