New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Incremental Learning of Functional Logic Programs
FLOPS '01 Proceedings of the 5th International Symposium on Functional and Logic Programming
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Strong Complete Schmema for Inductive Functional Logic Programming
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A survey on tree edit distance and related problems
Theoretical Computer Science
FLUX: functional updates for XML
Proceedings of the 13th ACM SIGPLAN international conference on Functional programming
An introduction to inductive programming
Artificial Intelligence Review
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In this work, we introduce a new distance function for data representations based on first-order logic (atoms, to be more precise) which integrates the main advantages of the distances that have been previously presented in the literature. Basically, our distance simultaneously takes into account some relevant aspects, concerning atom-based presentations, such as the position where the differences between two atoms occur (context sensitivity), their complexity (size of these differences) and how many times each difference occur (the number of repetitions). Although the distance is defined for first-order atoms, it is valid for any programming language with the underlying notion of unification. Consequently, many functional and logic programming languages can also use this distance.