CHIRP (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. Type: Data
    Training data

Output Ports

  1. Type: Weka 3.7 Classifier
    Trained model

Extension

This node is part of the extension

KNIME Weka Data Mining Integration (3.7)

v4.0.0

Short Link

Drag node into KNIME Analytics Platform