The DOI matches its Crossref record.
Learning subsequential transducers for pattern recognition interpretation tasks
Abstract
A formalization of the transducer learning problem and an effective and efficient method for the inductive learning of an important class of transducers, the class of subsequential transducers, are presented. The capabilities of subsequential transductions are illustrated through a series of experiments that also show the high effectiveness of the proposed learning method in obtaining very accurate and compact transducers for the corresponding tasks.>
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Cited by
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Probabilistic finite-state machines - part I
Vidal, E., Thollard, F., de la Higuera, C., Casacuberta, F., & Carrasco, R. C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.147 · Cited by 301
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Metadata
- Authors
- Oncina, J., Garcia, P., & Vidal, E.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1993-05
- Volume
- 15
- Issue
- 5
- Pages
- 448-458
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2162157640
- Cited by
- 198
- References
- 0
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Learning subsequential transducers for pattern recognition interpretation tasks. https://app.cochat.ai/papers/10.1109/34.211465
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- Oncina, J., Garcia, P., & Vidal, E. (1993). Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.211465
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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