99 search results - page 9 / 20 » Learning Hidden Markov Models with Distributed State Represe... |
NAACL
2004
13 years 9 months ago
2004
We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. W...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficult...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In ...
NN
1997
Springer
13 years 11 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
KDD
2002
ACM
14 years 8 months ago
2002
ACM
Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain ...