IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
The String-to-String Correction Problem
Journal of the ACM (JACM)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Text classification using string kernels
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
A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Parameter estimation for probabilistic finite-state transducers
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Protein homology detection using string alignment kernels
Bioinformatics
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Learning probabilistic models of tree edit distance
Pattern Recognition
A discriminative model of stochastic edit distance in the form of a conditional transducer
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Weighted symbols-based edit distance for string-structured image classification
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^' built from an alphabet @S requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over @S^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.