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Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Regularized negative correlation learning for neural network ensembles.

Huanhuan Chen1, Xin Yao

  • 1The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, Birmingham, UK. H.Chen@cs.bham.ac.uk

IEEE Transactions on Neural Networks
|November 20, 2009
PubMed
Summary
This summary is machine-generated.

Regularized Negative Correlation Learning (RNCL) addresses overfitting in neural network ensembles by adding regularization. RNCL improves performance over standard Negative Correlation Learning (NCL), especially with noisy data.

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Last Updated: Jun 18, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Negative Correlation Learning (NCL) is an ensemble method using a correlation penalty.
  • NCL's training objective, without regularization, can lead to overfitting noise in training data.

Purpose of the Study:

  • To analyze the overfitting issues in NCL.
  • To propose an improved algorithm, Regularized Negative Correlation Learning (RNCL), to mitigate overfitting.

Main Methods:

  • Analysis of NCL's training dynamics.
  • Introduction of an additional regularization term in RNCL.
  • Decomposition of ensemble training objectives into sub-objectives for individual networks.
  • Bayesian interpretation and automatic parameter optimization for RNCL.

Main Results:

  • NCL training (lambda=1) is equivalent to minimizing MSE without regularization, causing overfitting.
  • Cross-validation tuning of lambda in NCL does not solve overfitting.
  • RNCL effectively incorporates regularization and decomposes objectives.
  • RNCL demonstrates superior performance compared to NCL on synthetic and real-world datasets, particularly with high noise levels.

Conclusions:

  • RNCL offers a robust solution to NCL's overfitting problem.
  • The proposed Bayesian approach provides effective regularization parameter optimization.
  • RNCL is a versatile algorithm applicable to various nonlinear estimators minimizing MSE.