该模型以离散样本作为直接输入,采用卷积和算法实现对时间累积效应的处理。
This model utilizes discrete sampling points as input directly, and convolution sum to deal with time accumulation process.
决定性的因素是这种区分是没有根据的样本离散多时间,但它是一个物理作用,使用户会看到一个充满活力,低噪声加速度信号。
The decisive factor is that the differentiation is not based on a sample over a discrete time period, but is a physical effect, so that the user sees a dynamic, low-noise acceleration signal.
有效的离散化可以显著地提高系统对样本的聚类能力,增强系统对数据噪音的鲁棒性。
Effective data discretization can obviously improve system ability on clustering instances, and can also make systems more robust to data noise.
There's also another variance measure, which we use in the sample-- There's also another variance measure, which is for the sample.
还有另一个离散指标,我们用以考察样本,这是另一个离散指标,用于考察样本
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