A unified framework for connectionist systems
type: Item
instance of: scholarly article
title: A unified framework for connectionist systems
zbMATH Open document ID: 0667.92006
DOI: 10.1007/BF00317773
published in: Biological Cybernetics
publication date: 1988-01-01T00:00:00Z
review text: The paper proposes to treat connectionist systems (neural network models) within MAP (maximum a posteriori) estimation framework. The fundamental theorem assigns to every additive dynamical system summarizing function (specifying activity levels of visible neurons X for a given set of connection strengths) the subjective probability function representing the network's belief that X occurs in its environment. Classification and learning processes are then considered and evaluated from this viewpoint for the most popular neural networks paradigms [back-propagation; \textit{M. A. Cohen} and \textit{S. Grossberg} network, IEEE Trans. Syst. Man Cybern. 13, 815-826 (1983; Zbl 0553.92009); Hopfield model].
Mathematics Subject Classification ID: 92Cxx
Mathematics Subject Classification ID: 92F05
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T99
Mathematics Subject Classification ID: 62P99
zbMATH DE Number: 4093226
zbMATH Keywords: maximum likelihood estimation
zbMATH Keywords: MAP estimation
zbMATH Keywords: connectionist systems
zbMATH Keywords: neural network models
zbMATH Keywords: maximum a posteriori
zbMATH Keywords: additive dynamical system summarizing function
zbMATH Keywords: subjective probability function
zbMATH Keywords: Classification
zbMATH Keywords: learning processes
zbMATH Keywords: back-propagation
zbMATH Keywords: \textit{M. A. Cohen} and \textit{S. Grossberg} network
zbMATH Keywords: Hopfield model
author: Richard M. Golden
reviewed by: J.Horejs
MaRDI profile type: Publication
Wikidata QID: Q52253617
cites work: Q4092750
cites work: Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
cites work: Probability, Frequency and Reasonable Expectation
cites work: Q4403756
cites work: Q5595624
cites work: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
cites work: Q5558028
cites work: The ''brain-state-in-a-box'' neural model is a gradient descent algorithm
cites work: Neural networks and physical systems with emergent collective computational abilities.
cites work: Neurons with graded response have collective computational properties like those of two-state neurons.
cites work: Asymptotic Properties of Non-Linear Least Squares Estimators
cites work: Self-organization and associative memory
cites work: On Information and Sufficiency
cites work: The Covariance Matrix of the Information Matrix Test
cites work: Q3270181
cites work: A universal prior for integers and estimation by minimum description length
cites work: Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space
cites work: Q5599438
cites work: Maximum Likelihood Estimation of Misspecified Models
full work available at URL: https://doi.org/10.1007/bf00317773
OpenAlex ID: W1979878568
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description: scientific article; zbMATH DE number 4093226