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Fast probabilistic self-structuring of generalized single-layer networks.

R D Morris¹, A M Garvin

¹Inst. Nat. de Recherche en Inf. et Autom., Antipolis.

IEEE Transactions on Neural Networks

|January 1, 1996

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This study introduces an algorithm using Markov chain Monte Carlo sampling to identify essential basis functions for generalized single-layer networks (GSLNs) in classification tasks. It efficiently determines which functions are crucial for accurate data classification.

Area of Science:

Machine Learning
Computational Statistics
Network Science

Background:

Generalized Single-Layer Networks (GSLNs) are utilized for classification tasks.
Determining the optimal subset of basis functions is crucial for GSLN performance.
Existing methods may be computationally intensive or less efficient.

Purpose of the Study:

To develop an algorithm for identifying the essential basis functions in a GSLN for classification.
To employ a Markov chain Monte Carlo (MCMC) sampling technique for model space traversal.
To establish a method for assessing the importance of basis functions based on their inclusion frequency.

Main Methods:

An algorithm is presented to determine the subset of basis functions for a GSLN.

Related Experiment Videos

Markov chain Monte Carlo (MCMC) sampling is used to explore models with low sum squared error (SSE).

Fast, iterative updates are utilized for matrix calculations.

Main Results:

The frequency of a basis function's inclusion indicates its importance for the classifier.
Theoretical results provide guidance on the required MCMC chain length for effective discrimination.
Experimental validation confirms the theoretical findings.

Conclusions:

The proposed algorithm efficiently identifies critical basis functions for GSLN classification.
MCMC sampling offers a robust approach to navigating model spaces and assessing function importance.
The method provides a reliable way to distinguish between data-fitting functions and noise-modeling functions.