C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Global Cut Framework for Removing Symmetries
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A fast arc consistency algorithm for n-ary constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A study of residual supports in arc consistency
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalized arc consistency for positive table constraints
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Reformulating Positive Table Constraints Using Functional Dependencies
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Reformulating Global Grammar Constraints
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Encoding Table Constraints in CLP(FD) Based on Pair-Wise AC
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Minimising decision tree size as combinatorial optimisation
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Constraint representations and structural tractability
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Domain consistency with forbidden values
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Generating special-purpose stateless propagators for arbitrary constraints
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Dealing with Satisfiability and n-ary CSPs in a Logical Framework
Journal of Automated Reasoning
Exploiting short supports for generalised arc consistency for arbitrary constraints
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
An optimal filtering algorithm for table constraints
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Domain consistency with forbidden values
Constraints
Many-to-many interchangeable sets of values in CSPs
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Eliminating redundancy in CSPs through merging and subsumption of domain values
ACM SIGAPP Applied Computing Review
Short and long supports for constraint propagation
Journal of Artificial Intelligence Research
Extending simple tabular reduction with short supports
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present an algorithm for compressing table constraints representing allowed or disallowed tuples. This type of constraint is used for example in configuration problems, where the satisfying tuples are read from a database. The arity of these constraints may be large. A generic GAC algorithm for such a constraint requires time exponential in the arity of the constraint to maintain GAC, but Bessière and Régin showed in [1] that for the case of allowed tuples, GAC can be enforced in time proportional to the number of allowed tuples, using the algorithm GAC-Schema. We introduce a more compact representation for a set of tuples, which allows a potentially exponential reduction in the space needed to represent the satisfying tuples and exponential reduction in the time needed to enforce GAC. We show that this representation can be constructed from a decision tree that represents the original tuples and demonstrate that it does in practice produce a significantly shorter description of the constraint. We also show that this representation can be efficiently used in existing algorithms and can be used to improve GAC-Schema further. Finally, we show that this method can be used to improve the complexity of enforcing GAC on a table constraint defined in terms of forbidden tuples.