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arXiv · 2111.14574

On the rate of convergence of a classifier based on a Transformer encoder

Iryna Gurevych·Michael Kohler·Gözde Gül Sahin
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Abstract

Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the aposteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between Transformer classifiers analyzed theoretically in this paper and Transformer classifiers used nowadays in practice are illustrated by considering classification problems in natural language processing.

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Iryna Gurevych, Michael Kohler, Gözde Gül Sahin. 2021-11-29. On the rate of convergence of a classifier based on a Transformer encoder. https://arxiv.org/abs/2111.14574

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