/Measuring-VC-Dimension

Measuring the VC-dimension using experimental method.

Primary LanguagePython

Measuring the VC-Dimension Using Experimental Method

This repository contains a Python implementation of "Measuring the VC-Dimension Using Optimized Experimental Design" by Shao, Cherkassky, and Li (2000). This code measures the effective VC-dimension of a linear regression model using experimental method.

Installation

To run this code, you need to have Python 3.x installed on your system. You also need to have the following Python packages installed:

  • numpy
  • scikit-learn

You can install these packages using the following command:

pip install numpy scikit-learn

Usage

To use this code, you can simply run the vc_dimension.py file. This will generate a random dataset, train a linear regression model on it, and estimate the effective VC-dimension of the model using experimental method.

python vc_dimension.py

The output will be the estimated effective VC-dimension of the linear regression model.

References

  • Shao, X., Cherkassky, V., & Li, W. (2000). Measuring the VC-dimension using optimized experimental Design. Neural Computation, 12(8), 1969-1986.