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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions; 28th International C Igor V. Tetko,Věra K?rkov

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發(fā)表于 2025-3-23 12:47:21 | 只看該作者
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Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computerrol, and analysis. Deep learning achieved remarkable results, but remains hard to train in practice. Here, we propose a photonic reservoir computer for recognition of video-based human actions. Our experiment comprises off-the-shelf components and implements an easy-to-train neural network, scalable
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發(fā)表于 2025-3-24 07:30:07 | 只看該作者
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發(fā)表于 2025-3-24 13:52:43 | 只看該作者
https://doi.org/10.1007/978-3-030-30493-5artificial intelligence; classification; clustering; computer networks; echo state networks; image proces
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發(fā)表于 2025-3-24 18:03:32 | 只看該作者
978-3-030-30492-8Springer Nature Switzerland AG 2019
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發(fā)表于 2025-3-24 19:21:47 | 只看該作者
https://doi.org/10.1007/978-3-319-68883-1 be extracted from the inertial measurement unit of a mobile phone and introduce a segmentation scheme to distinguish between different gesture classes. The continuous sequences are fed into an Echo State Network, which learns sequential data fast and with good performance. We evaluated our system o
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發(fā)表于 2025-3-24 23:59:24 | 只看該作者
Hua-Xin Peng,Faxiang Qin,Manh-Huong Phane constant temporal patterns. For the short-term component, we used the Gated-Reservoir model: a reservoir trained to hold a triggered information from an input stream and maintain it in a readout unit. We combined both components in order to obtain a model in which information can go from long-term
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