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ICML
2008
IEEE
16 years 17 days ago
Training restricted Boltzmann machines using approximations to the likelihood gradient
A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Diverg...
Tijmen Tieleman
ICML
2006
IEEE
16 years 17 days ago
Accelerated training of conditional random fields with stochastic gradient methods
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...
ADCM
2000
45views more  ADCM 2000»
14 years 11 months ago
Training neural networks with noisy data as an ill-posed problem
This paper is devoted to the analysis of network approximation in the framework of approximation and regularization theory. It is shown that training neural networks and similar n...
Martin Burger, Heinz W. Engl
ICDT
2003
ACM
139views Database» more  ICDT 2003»
15 years 5 months ago
New Rewritings and Optimizations for Regular Path Queries
All the languages for querying semistructured data and the web use as an integral part regular expressions. Based on practical observations, finding the paths that satisfy those r...
Gösta Grahne, Alex Thomo
TAL
2004
Springer
15 years 5 months ago
Unsupervised Training of a Finite-State Sliding-Window Part-of-Speech Tagger
A simple, robust sliding-window part-of-speech tagger is presented and a method is given to estimate its parameters from an untagged corpus. Its performance is compared to a standa...
Enrique Sánchez Villamil, Mikel L. Forcada,...