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Related Experiment Video

Updated: Jul 11, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

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A model selection algorithm for a posteriori probability estimation with neural networks.

Juan Ignacio Arribas1, Jesús Cid-Sueiro

  • 1Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, 47011 Valladolid, Spain. jarribas@tel.uva.es

IEEE Transactions on Neural Networks
|August 27, 2005
PubMed
Summary

This study introduces a novel algorithm for neural network (NN) model selection, enhancing structure and parameter determination. The a posteriori probability model selection (PPMS) algorithm optimizes neural networks for probabilistic outputs.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Determining optimal neural network (NN) structure and parameters is crucial for model performance.
  • Existing methods often lack efficient joint optimization of structure and probabilistic parameters.
  • Probabilistic interpretation of NN components offers potential for improved model selection.

Purpose of the Study:

  • To propose a novel algorithm for joint determination of NN structure and a posteriori probability model parameters.
  • To introduce the a posteriori probability model selection (PPMS) algorithm.
  • To apply and evaluate PPMS on the generalized softmax perceptron (GSP) architecture.

Main Methods:

  • Utilizes pruning, splitting, and merging of neural components, leveraging their probabilistic interpretation.
  • Applies the expectation-maximization algorithm to derive learning rules for the GSP-PPMS structure.
  • Extends the approach to more general NN architectures beyond the GSP.

Main Results:

  • The proposed PPMS algorithm effectively determines both the structure and parameters of a posteriori probability models.
  • Simulation results demonstrate the advantages of PPMS over existing model selection schemes.
  • The GSP architecture, when combined with PPMS, yields outputs interpretable as probabilities.

Conclusions:

  • The PPMS algorithm offers a robust and effective method for neural network model selection.
  • This approach enhances the probabilistic modeling capabilities of neural networks.
  • The PPMS algorithm shows significant promise for improving NN performance in probabilistic tasks.