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Tìm kiếm theo: Tác giả Matthieu, Herrmann

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    • Ultra-fast meta-parameter optimization for time series similarity measures with application to nearest neighbour classification
    Tác giả : Chang Wei, Tan; Matthieu, Herrmann; Geoffrey I., Webb;  Người hướng dẫn: -;  Đồng tác giả: - (2023)

    Nearest neighbour similarity measures are widely used in many time series data analysis applications. They compute a measure of similarity between two time series. Most applications require tuning of these measures’ meta-parameters in order to achieve good performance. However, most measures have at least O(L2) complexity, making them computationally expensive and the process of learning their meta-parameters burdensome, requiring days even for datasets containing only a few thousand series. In this paper, we propose ULTRAFASTMPSEARCH, a family of algorithms to learn the meta-parameters for different types of time series distance measures. These algorithms are significantly faster t...