A unified framework for connectionist systems
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].
additive dynamical system summarizing function
\textit{M. A. Cohen} and \textit{S. Grossberg} network
scientific article; zbMATH DE number 4093226
scientific article