Abstract
A recent result in the literature states that polynomial and conjunctive features can be hierarchically organized and described by different kernels of increasing expressiveness (or complexity). Additionally, the optimal combination of those kernels through a Multiple Kernel Learning approach produces effective and robust deep kernels. In this paper, we extend this approach to structured data, showing an adaptation of classical spectrum kernels, here named monotone spectrum kernels, reflecting a hierarchical feature space of sub-structures of increasing complexity. Finally, we show that (i) our kernels adaptation does not differ significantly from classical spectrum kernels, and (ii) the optimal combination achieves better results than the single spectrum kernel.
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Notes
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This value is practically bounded by the length of the longer string in the dataset.
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Note that this task is different from classical Named Entity Recognition, where the whole sentence is simultaneously observed.
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Lauriola, I., Aiolli, F. (2020). Monotone Deep Spectrum Kernels. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_17
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DOI: https://doi.org/10.1007/978-3-030-61609-0_17
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