TY - JOUR
T1 - Performance of the Bayesian online algorithm for the perceptron
AU - de Oliveira, Evaldo Araújo
AU - Alamino, Roberto C.
PY - 2007/5
Y1 - 2007/5
N2 - 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.
AB - 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.
KW - Bayesian algorithms
KW - online gradient methods
KW - pattern classification
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4182376
UR - http://www.scopus.com/inward/record.url?scp=34248663836&partnerID=8YFLogxK
U2 - 10.1109/TNN.2007.891189
DO - 10.1109/TNN.2007.891189
M3 - Letter, comment/opinion or interview
C2 - 17526354
AN - SCOPUS:34248663836
SN - 1045-9227
VL - 18
SP - 902
EP - 905
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 3
ER -