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- W4386927113 abstract "Sequences are often not received in their entirety at once, but instead, received incrementally over time, element by element. Early predictions yielding a higher benefit, one aims to classify a sequence as accurately as possible, as soon as possible, without having to wait for the last element. For this early sequence classification, we introduce our novel classifier-induced stopping. While previous methods depend on exploration during training to learn when to stop and classify, ours is a more direct, supervised approach. Our classifier-induced stopping achieves an average Pareto frontier AUC increase of 11.8% over multiple experiments." @default.
- W4386927113 created "2023-09-22" @default.
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- W4386927113 date "2023-01-01" @default.
- W4386927113 modified "2023-10-16" @default.
- W4386927113 title "A Policy for Early Sequence Classification" @default.
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- W4386927113 doi "https://doi.org/10.1007/978-3-031-44207-0_5" @default.
- W4386927113 hasPublicationYear "2023" @default.
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