The logic of typed feature structures
The logic of typed feature structures
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
An Axiomatic Approach to Feature Term Generalization
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Cases as terms: A feature term approach to the structured representation of cases
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Theta-Subsumption in a Constraint Satisfaction Perspective
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Constraints-driven scheduling and resource assignment
ACM Transactions on Design Automation of Electronic Systems (TODAES)
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Inductive inference of VL decision rules
ACM SIGART Bulletin
Structured machine learning: the next ten years
Machine Learning
On Similarity Measures Based on a Refinement Lattice
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Breaking symmetries in all different problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Similarity measures over refinement graphs
Machine Learning
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Feature Terms are a generalization of first-order terms which have been recently received increased attention for their usefulness in structured machine learning applications. One of the main obstacles for their wide usage is that their basic operation, subsumption, has a very high computational cost. Constraint Programming is a very suitable technique to implement that operation, in some cases providing orders of magnitude speed-ups with respect to the standard subsumption approach. In addition, exploiting a basic variable symmetry ---that often appears in Feature Terms databases--- causes substantial additional savings. We provide experimental results of the benefits of this approach.