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ML
1998
ACM
139views Machine Learning» more  ML 1998»
15 years 6 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
ICML
2005
IEEE
16 years 7 months ago
A causal approach to hierarchical decomposition of factored MDPs
We present Variable Influence Structure Analysis, an algorithm that dynamically performs hierarchical decomposition of factored Markov decision processes. Our algorithm determines...
Anders Jonsson, Andrew G. Barto
NIPS
2004
15 years 8 months ago
Hierarchical Clustering of a Mixture Model
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original mod...
Jacob Goldberger, Sam T. Roweis
JDWM
2006
84views more  JDWM 2006»
15 years 6 months ago
Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data
This paper focuses on the discovery of surprising, unexpected patterns, based on a data mining method that consists of detecting instances of Simpson's paradox. By its very n...
Carem C. Fabris, Alex Alves Freitas
CGF
2005
106views more  CGF 2005»
15 years 6 months ago
Hierarchical Penumbra Casting
We present a novel algorithm for rendering physically-based soft shadows in complex scenes. Instead of casting shadow rays, we place both the points to be shaded and the samples o...
Samuli Laine, Timo Aila