Equivalence between Weight Decay Learning and Explicit Regularization to Improve Fault Tolerance of RBF
碩士 === 朝陽科技大學 === 網路與通訊研究所 === 96 === This thesis studies the fault tolerant performance of a radial basis function (RBF) network being trained to against the multiplicative weight noise (MWN) using both weight decay learning (WDL) and explicit regularization learning (ERL). It is shown analytical t...
| Main Authors: | , |
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/91062868963543008427 |