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- W1507044462 abstract "We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than a RBF network or a MLP. Results for various data sizes on the Pumadyn data indicate that the resulting architecture competes and often outperform best known results for this data set." @default.
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- W1507044462 date "2002-01-01" @default.
- W1507044462 modified "2023-10-16" @default.
- W1507044462 title "Forward and Backward Selection in Regression Hybrid Network" @default.
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- W1507044462 doi "https://doi.org/10.1007/3-540-45428-4_10" @default.
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