Performance of the Bayesian online algorithm for the perceptron

de Oliveira, Evaldo Araújo and Alamino, Roberto C. (2007). Performance of the Bayesian online algorithm for the perceptron. IEEE Transactions on Neural Networks and Learning Systems, 18 (3), pp. 902-905.

Abstract

In this letter, we derive continuum equations for the generalization error of the Bayesian online algorithm (BOnA) for the one-layer perceptron with a spherical covariance matrix using the Rosenblatt potential and show, by numerical calculations, that the asymptotic performance of the algorithm is the same as the one for the optimal algorithm found by means of variational methods with the added advantage that the BOnA does not use any inaccessible information during learning.

Publication DOI: https://doi.org/10.1109/TNN.2007.891189
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Non-linearity and complexity research group
Engineering & Applied Sciences
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Uncontrolled Keywords: Bayesian algorithms,online gradient methods,pattern classification,Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Control and Systems Engineering,Electrical and Electronic Engineering,Theoretical Computer Science
Published Date: 2007-05

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