Performance of the Bayesian online algorithm for the perceptron

Evaldo Araújo de Oliveira, Roberto C. Alamino

Research output: Contribution to journalLetter, comment/opinion or interviewpeer-review


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.

Original languageEnglish
Pages (from-to)902-905
Number of pages4
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number3
Publication statusPublished - May 2007


  • Bayesian algorithms
  • online gradient methods
  • pattern classification


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