PNN Learner (DDA)

Trains a Probabilistic Neural Network (PNN) based on the DDA (Dynamic Decay Adjustment) method on labeled data using Constructive Training of Probabilistic Neural Networks as the underlying algorithm.
This algorithm generates rules based on numeric data. Each rule is defined as high-dimensional Gaussian function that is adjusted by two thresholds, theta minus and theta plus, to avoid conflicts with rules of different classes. Each Gaussian function is defined by a center vector (from the first covered instance) and a standard deviation which is adjusted during training to cover only non-conflicting instances. The selected numeric columns of the input data are used as input data for training and additional columns are used as classification target, either one column holding the class information or a number of numeric columns with class degrees between 0 and 1 can be selected. The data output contains the rules after execution along with a number of of rule measurements. The model output port contains the PNN model, which can be used for prediction in the PNN Predictor node.

Input ports

1. Type: Table
   Training Data
   Numeric data as well as class information used for training.

Output ports

1. Type: Table
   PNN Rules
   Rules as Gaussian functions, classification columns, and additional rule measures.
2. Type: Radial Basis Function
   PNN Model
   PNN model can be used for prediction.