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LSTM-Human-Activity-Recognition-master

于 2019-06-13 发布 文件大小:260KB
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  与经典的方法相比,使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中,神经网络就像一个黑匣子,可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程,这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing)

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