135 search results - page 1 / 27 » Hilbert Space Embeddings of Hidden Markov Models |
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
ICPR
2008
IEEE
13 years 11 months ago
2008
IEEE
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
SSPR
2010
Springer
13 years 2 months ago
2010
Springer
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
NIPS
2003
13 years 6 months ago
2003
We describe a Markov chain method for sampling from the distribution of the hidden state sequence in a non-linear dynamical system, given a sequence of observations. This method u...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connecti...