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Linear classifier using random convolutional kernels applied to time series.
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Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.
Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
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Linear classifier using random convolutional kernels applied to time series.
InceptionTime: Finding AlexNet for Time Series Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.