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LMNC
[W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
| Input | |
| A | Dataset |
| UNITS | Vector with numbers of units in each hidden layer (default: [5]) |
| ITER | Number of iterations to train (default: inf) |
| W_INI | Weight initialization network mapping (default: [], meaning initialization by Matlab's neural network toolbox) |
| T | Tuning set (default: [], meaning use A) |
| Output | |
| W | Trained feed-forward neural network mapping |
| HIST | Progress report (see below) |
A feed-forward neural network classifier with length(N) hidden layers with N(I) units in layer I is computed for the dataset A. Training is stopped after ITER epochs (at least 50) or if the iteration number exceeds twice that of the best classification result. This is measured by the labeled tuning set T. If no tuning set is supplied A is used. W_INI is used, if given, as network initialization. Use [] if the standard Matlab initialization is desired. Progress is reported in file FID (default 0).
The entire training sequence is returned in HIST (number of epochs, classification error on A, classification error on T, MSE on A, MSE on T, mean of squared weights).
Uses the Mathworks' Neural Network toolbox.
mappings, datasets, bpxnc, neurc, rnnc, rbnc, prprogress,
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