Learning stochastic edit distance: Application in handwritten character recognition
✍ Scribed by Jose Oncina; Marc Sebban
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 276 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
✦ Synopsis
Many pattern recognition algorithms are based on the nearest-neighbour search and use the well-known edit distance, for which the primitive edit costs are usually fixed in advance. In this article, we aim at learning an unbiased stochastic edit distance in the form of a finite-state transducer from a corpus of (input, output) pairs of strings. Contrary to the other standard methods, which generally use the Expectation Maximisation algorithm, our algorithm learns a transducer independently on the marginal probability distribution of the input strings. Such an unbiased way to proceed requires to optimise the parameters of a conditional transducer instead of a joint one. We apply our new model in the context of handwritten digit recognition. We show, carrying out a large series of experiments, that it always outperforms the standard edit distance.