... desired percentage return 预先期待回报率 categorization of perception 感知分类 pensation/reards 酬金/奖励 ...
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应用感知器的线性分类 Linear classification using the perceptron
它证明了第二个假设——我们确实一直感知着所有事,但大脑对干扰的分类不同——也不是真实的。
It proved that the second hypothesis—that we do perceive everything all the time but the brain categorizes distractions differently—wasn't true either.
第二个假设是,我们确实能够感知一切,但是大脑会对信息进行分类,而任何与我们专注的事情无关的信息都会被归类为低优先级信息。
The second hypothesis is that we do perceive everything, but the brain categorizes the information, and whatever is not relevant to what we are concentrating on gets treated as low priority.
例如,某些基本的神经网络,它们的感知器只倾向于学习线形函数(通过划一条线可以把函数输入解析到分类系统中)。
For instance, a certain kind of basic neural network, the perceptron, is biased to learning only linear functions (functions with inputs that can be separated into classifications by drawing a line).
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