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CHIRP (3.7)

AnalyticsIntegrationsWekaWeka (3.7)Classification Algorithms
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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.

Node details

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

Extension

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