Good Similarity Learning for Structured Data
Aurélien Bellet, Amaury Habrard and Marc Sebban
In: NIPS 2011 Workshop "Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity", 16 Dec 2011, Sierra Nevada, Spain.

Abstract

Similarity functions play an important role in the performance of many learning algorithms, thus a lot of research has gone into training them. In this paper, we focus on learning similarity functions for structured data. We propose a novel edit similarity learning approach (GESL) driven by the idea of (e,g,t)-goodness, a recent theory that bridges the gap between the properties of a similarity function and its performance in classification. We derive generalization guarantees for our method and provide experimental evidence of its practical interest.

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