Performance of the Bayesian online algorithm for the perceptron.
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.
de Oliveira
Evaldo Araújo
EA
Alamino
Roberto Castro
RC
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*