Round Randomized Learning Vector Quantization for Brain Tumor Imaging

Table 3

MLP, SOM, and RBF configuration.

MLP	RBF	SOM	Random Forest
Decay = true; Hidden layers = 5; LR = 0.3; Momentum = 0.2; Training time = 2000; Validation threshold = 20	Seed = 1. The random seed to pass on to -means.; maxIts = −1. Maximum number of iterations for the logistic regression to perform. Only applied to discrete class problems.; minStdDev = 0.1. Sets the minimum standard deviation for the clusters.; numClusters = 2. The number of clusters for -means to generate.; Ridge = . Set the ridge value for the logistic or linear regression.	Learning function = linear decay (tangen); Learning rate = 0.3; Map height = 6; Map width = 8; Neighborhood function = Gaussian; Neighborhood size = 8; Seed = 1; Supervised = false; Number of iterations = 2000	Debug = true; Maximum depth = 0; Number of features = 1; Seed = 1