1. One Hidden Layer (ALWAYS SUFFICIENT!!!) 2. Minimum Number of Hidden Nodes subject to my practicality constraint TRAINING SUBSET RSQUARE >= 0.99 i.e. 99% of the training subset target variance is successfully modeled by the net. Equivalently TRAINING SUBSET MSE <= 0.01*TRAINING SUBSET VARIANCE 3. COMMENTS & CAVEATS a. The training subset must be a good representative of validation and test data b. A smaller number of hidden nodes can often be obtained by using multiple hidden layers c. The MSE minimization technique used for regression and curvefitting (e.g., via FITNET)is also successful for classification and pattern recognition (e.g., via PATTERNNET) where the minimization function is cross-entropy and the desired result is minimal error rate.
greg fitnet/patternnet msegoal nmse
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