TY - GEN
T1 - Composite likelihood estimation for restricted Boltzmann machines
AU - Yasuda, Muneki
AU - Kataoka, Shun
AU - Waizumi, Yuji
AU - Tanaka, Kazuyuki
PY - 2012
Y1 - 2012
N2 - Generally, learning the parameters of graphical models by using the maximum likelihood estimation is difficult and requires an approximation. Maximum composite likelihood estimations are statistical approximations of the maximum likelihood estimation and are higher-order generalizations of the maximum pseudo-likelihood estimation. In this paper, we propose a composite likelihood method and investigate its properties. Furthermore, we apply this to restricted Boltzmann machines.
AB - Generally, learning the parameters of graphical models by using the maximum likelihood estimation is difficult and requires an approximation. Maximum composite likelihood estimations are statistical approximations of the maximum likelihood estimation and are higher-order generalizations of the maximum pseudo-likelihood estimation. In this paper, we propose a composite likelihood method and investigate its properties. Furthermore, we apply this to restricted Boltzmann machines.
UR - http://www.scopus.com/inward/record.url?scp=84874564218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874564218&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84874564218
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2234
EP - 2237
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -