Algorithms for approximate string matching
Information and Control
Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Algorithms for finding patterns in strings
Handbook of theoretical computer science (vol. A)
Conceptual clustering in a first order logic representation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial Intelligence Review - Special issue on lazy learning
Principles of knowledge representation
Lazy learning
The String-to-String Correction Problem
Journal of the ACM (JACM)
The Tree-to-Tree Correction Problem
Journal of the ACM (JACM)
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Term Comparisons in First-Order Similarity Measures
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Stochastic Simple Similarity
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Distances and Limits on Herbrand Interpretations
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance-based learner ribl that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and empirically demonstrate its additional power on a problem from the domain of biochemistry. The paper also includes a thorough reconstruction of ribl's overall algorithm.