NodeCHIRP (3.7)

Learner

CHIRP is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability

CHIRP classifies with a set cover on iterated random projections.For more information, see: CHIRP: A New Classifier Based on Composite Hypercubes on Iterated Random Projections.

Proceedings of the ACM KDD 2011.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input ports
  1. Training data Type: Data
    Training data
Output ports
  1. Trained model Type: Weka 3.7 Classifier
    Trained model