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UAI
1998
15 years 8 months ago
Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Nir Friedman, Kevin P. Murphy, Stuart J. Russell
COLING
1996
15 years 8 months ago
Decision Tree Learning Algorithm with Structured Attributes: Application to Verbal Case Frame Acquisition
The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research comlnunity, and there have been a series of attempts to appl...
Hideki Tanaka
BILDMED
2009
251views Algorithms» more  BILDMED 2009»
15 years 7 months ago
Probabilistic Tracking and Model-Based Segmentation of 3D Tubular Structures
Abstract. We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and...
Stefan Wörz, William J. Godinez, Karl Rohr
ICASSP
2010
IEEE
15 years 6 months ago
Structuring a gene network using a multiresolution independence test
In order to structure a gene network, a score-based approach is often used. A score-based approach, however, is problematic because by assuming a probability distribution, one is ...
Takayuki Yamamoto, Tetsuya Takiguchi, Yasuo Ariki
COGSCI
2010
107views more  COGSCI 2010»
15 years 6 months ago
Inferring Hidden Causal Structure
We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a ...
Tamar Kushnir, Alison Gopnik, Chris Lucas, Laura S...