IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On a theory of learning with similarity functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
On learning with dissimilarity functions
Proceedings of the 24th international conference on Machine learning
Learning probabilistic models of tree edit distance
Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Bayesian Similarity Model Estimation for Approximate Recognized Text Search
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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Similarity and distance functions are essential to many learning algorithms, thus training them has attracted a lot of interest. When it comes to dealing with structured data (e.g., strings or trees), edit similarities are widely used, and there exists a few methods for learning them. However, these methods offer no theoretical guarantee as to the generalization performance and discriminative power of the resulting similarities. Recently, a theory of learning with (ε, γ, τ)-good similarity functions was proposed. This new theory bridges the gap between the properties of a similarity function and its performance in classification. In this paper, we propose a novel edit similarity learning approach (GESL) driven by the idea of (ε, γ, τ)-goodness, which allows us to derive generalization guarantees using the notion of uniform stability. We experimentally show that edit similarities learned with our method induce classification models that are both more accurate and sparser than those induced by the edit distance or edit similarities learned with a state-of-the-art method.