146 search results - page 20 / 30 » Online Gradient Descent Learning Algorithms |
ETVC
2008
13 years 9 months ago
2008
In a seminal paper, Amari (1998) proved that learning can be made more efficient when one uses the intrinsic Riemannian structure of the algorithms' spaces of parameters to po...
EAAI
2006
13 years 7 months ago
2006
This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required abou...
CVPR
2009
IEEE
15 years 2 months ago
2009
IEEE
Many computer vision problems can be formulated in
a Bayesian framework with Markov Random Field (MRF)
or Conditional Random Field (CRF) priors. Usually, the
model assumes that ...
CORR
2011
Springer
12 years 11 months ago
2011
Springer
Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks,...
NIPS
1994
13 years 8 months ago
1994
A new learning algorithmis derived which performs online stochastic gradient ascent in the mutual informationbetween outputs and inputs of a network. In the absence of a priori kn...