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- W2132277164 abstract "The effects is studied on the convergence properties of the back-propagation learning rule of the range from which the initial weight values are randomly selected. In addition to the standard back-propagation rule, two variations are also considered, namely symmetric back-propagation and expected-value back-propagation. In most applications of back-propagation, the range of initial weights is small. It is shown that significantly higher initial weights can substantially improve learning rates. If the initial weight range is increased beyond a problem-dependent limit, however, performance degrades. Symmetric back-propagation is most sensitive to the initial weight range, while expected value back-propagation is least sensitive. The authors describe an improvement on the symmetric variation that produces faster learning rates with low initial weights.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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- W2132277164 date "2003-01-13" @default.
- W2132277164 modified "2023-09-27" @default.
- W2132277164 title "Effect of initial weights on back-propagation and its variations" @default.
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- W2132277164 doi "https://doi.org/10.1109/icsmc.1989.71283" @default.
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