Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Markov Random Walk Representations with Continuous Distributions
Chen-Hsiang Yeang, Martin Szummer
Abstract:
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the data density. We relate this diffusion equation to a path integral and derive the corresponding path probability measure. The framework is useful for incorporating continuous data densities and prior knowledge.
Keywords:
Pages: 600-607
PS Link:
PDF Link: /papers/03/p600-yeang.pdf
BibTex:
@INPROCEEDINGS{Yeang03,
AUTHOR = "Chen-Hsiang Yeang and Martin Szummer",
TITLE = "Markov Random Walk Representations with Continuous Distributions",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "600--607"
}


hosted by DSL   •   site info   •   help