...and the perceptron is better than its reputation after all!

A. Faessler
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Abstract

A large class of functions (in one or more variables) can be approximated by a, generally, multilayer feed-forward network, in which only the weights of the last layer need to be trained. All others can be selected appropriately dependent upon the desired accuracy with which the training examples are to be approximated, but independently of the examples. Thus only a perceptron remains to be trained.
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…毕竟感知器比它的名声要好!
一个大的函数类(在一个或多个变量中)通常可以用多层前馈网络来近似,其中只需要训练最后一层的权重。所有其他的都可以适当地选择,这取决于要近似的训练样例的所需精度,但与样例无关。因此,只剩下一个感知器有待训练。
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