Title :
Performance of the Bayesian Online Algorithm for the Perceptron
Author :
de Oliveira, E.A. ; Alamino, R.C.
Author_Institution :
Sao Paulo Univ.
fDate :
5/1/2007 12:00:00 AM
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
Keywords :
Bayes methods; covariance matrices; perceptrons; Bayesian online algorithm; Rosenblatt potential; continuum equations; generalization error; one-layer perceptron; spherical covariance matrix; variational methods; Algorithm design and analysis; Approximation algorithms; Bayesian methods; Covariance matrix; Equations; Gradient methods; Machine learning; Machine learning algorithms; Parameter estimation; Pattern classification; Bayesian algorithms; online gradient methods; pattern classification; Algorithms; Artificial Intelligence; Bayes Theorem; Computer Simulation; Decision Support Techniques; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Online Systems; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891189
