Bounds for the computational power and learning complexity of analog neural nets.
It is shown that high-order feedforward neural nets of constant depth with piecewise-polynomial activation functions and arbitrary real weights can be simulated for Boolean inputs and outputs by neural nets of a somewhat larger size and depth with Heaviside gates and weights from {-1,0,1}. This prov...
| 发表在: | SIAM journal on computing. 26, 3 (1997). |
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| 格式: | 文件 |
| 语言: | English |
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