PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Experimental Study on Learning with Good Edit Similarity Functions
Aurélien Bellet, Amaury Habrard and Marc Sebban
In: ICTAI 2011, 7-9 Nov 2011, Boca Raton, USA.

Abstract

Similarity functions are essential to many learning algorithms. To allow their use in support vector machines (SVM), i.e., for the convergence of the learning algorithm to be guaranteed, they must be valid kernels. In the case of structured data, the similarities based on the popular edit distance often do not satisfy this requirement, which explains why they are typically used with k-nearest neighbor (k-NN). A common approach to use such edit similarities in SVM is to transform them into potentially (but not provably) valid kernels. Recently, a different theory of learning with (e,g,t)-good similarity functions was proposed, allowing the use of non-kernel similarity functions. Moreover, the resulting models are supposedly sparse, as opposed to standard SVM models that can be unnecessarily dense. In this paper, we study the relevance and applicability of this theory in the context of string edit similarities. We show that they are naturally good for a given string classification task and provide experimental evidence that the obtained models not only clearly outperform the k-NN approach, but are also competitive with standard SVM models learned with state-of-the-art edit kernels, while being much sparser.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8517
Deposited By:Aurélien Bellet
Deposited On:09 February 2012