Abstract
One-dimensional (string) formal languages and their learning have been studied in considerable depth. However, the knowledge of their two-dimensional (picture) counterpart, which retains similar importance, is lacking. We investigate the problem of learning formal two-dimensional picture languages by applying learning methods for one-dimensional (string) languages. We formalize the transcription process from an input two-dimensional picture into a string and propose a few adaptations to it. These proposals are then tested in a series of experiments, and their outcomes are compared. Finally, these methods are applied to a practical problem and learn an automaton for recognizing a part of the MNIST dataset. The obtained results show improvements in the topic and the potential in using the learning of automata in fitting problems.
Supported by Charles University Grant Agency (GAUK) project no. 1198519 and SVV-260588.
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Notes
- 1.
The dataset is available from http://yann.lecun.com/exdb/mnist.
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Acknowledgements
This research was supported by the Charles University Grant Agency (GAUK) project no. 1198519 and SVV-260588.
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Kuboň, D., Mráz, F., Rychtera, I. (2022). Learning Automata Using Dimensional Reduction. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_4
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