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Related Experiment Videos

On overfitting, generalization, and randomly expanded training sets.

G N Karystinos¹, D A Pados

¹Department of Electrical Engineering, State University of New York, Buffalo, NY 14260-2050, USA. cary@eng.buffalo.edu

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

This study introduces a novel algorithmic procedure to expand training data, enhancing the generalization of multilayer perceptrons (MLPs) by reducing overfitting. The method uses K-means clustering and Gaussian density estimation for improved model performance.

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Area of Science:

Machine Learning
Artificial Intelligence
Pattern Recognition

Background:

Overfitting in multilayer perceptrons (MLPs) hinders generalization.
Backpropagation training requires robust datasets to avoid poor performance.

Purpose of the Study:

To develop an algorithmic procedure for random training set expansion.
To combat overfitting and enhance MLP generalization ability.

Main Methods:

K-means clustering of the training set.
Formation of locally most entropic colored Gaussian joint input-output probability density function (pdf) estimates per cluster.
Selection of the optimal number of clusters via minimum differential entropy and global cross-validation.

Main Results:

Demonstrated effectiveness of the proposed algorithmic procedure.
Improved generalization ability of backpropagation trained MLPs.
Validation through numerical studies on real and synthetic data.

Conclusions:

The developed procedure effectively expands training data to mitigate overfitting.
The method enhances the generalization capabilities of MLPs.
The approach is supported by empirical evidence from diverse datasets.