Weights in Neural networks

John Williams answered . 2024-12-21 16:29:55

    a. The number of input variables, I, is large.

    b. Some input variables are correlated.

    c. The number of hidden nodes, H, is large.

    d. The number of output variables, O, is large.

    a. Use STEPWISEFIT or SEQUENTIALFS with polynomial models that are 
    linear in the weights.

    b. After training, rank the inputs by the increase in MSE when only the
    matrix row of that input is scrambled (i.e., randomly reordered ). Remove
    the worst input, retrain and repeat untill only useful inputs remain.

    c.Transform to dominant orthogonal inputs using PCA for regression or PLS 
    for classification.

    a. Dividing SSE by the degree-of-freedom adjusted denominator Neqtrn-Nw 
    (instead of Ntrneq) or

    b. Using a separate holdout validation set ( which is not necessarily used for validation stopping)

    Hope this helps.

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