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
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.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to firstname.lastname@example.org, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.