Feed-forward neural net classifier back-end
[W,HIST,UNITS] = FFNC(ALG,A,UNITS,ITER,W_INI,T,FID)
| ALG|| Training algorithm: 'bpxnc' for back-propagation, 'lmnc' for Levenberg-Marquardt|
| A|| Training dataset|
| UNITS|| Array indicating number of units in each hidden layer. Default is a single hidden layer. Its size is the half of the number of objects in A divided by feature size plus class size (roughly half of the number of parameters to be optimised) with a maximum of 100; |
| ITER|| Number of iterations to train (default: inf)|
| W_INI|| Weight initialisation network mapping (default: , meaning initialisation by Matlab's neural network toolbox)|
| T|| Tuning set (default: , meaning use A) |
| FID|| File ID to write progress to (default , see PRPROGRESS) |
| W|| Trained feed-forward neural network mapping|
| HIST|| Progress report (see below)|
This function should not be called directly, but through one of its front-ends, BPXNC or LMNC. Uses the Mathworks' Neural Network toolbox.
This routine escapes to KNNC if any class has less than 3 objects.
mappings, datasets, bpxnc, lmnc, neurc, rnnc, rbnc, knnc,
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|