311 search results - page 29 / 63 » Learning Probabilistic Models of Link Structure |
ICML
2008
IEEE
16 years 19 days ago
2008
IEEE
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
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NIPS
1998
15 years 1 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
BIOINFORMATICS
2006
14 years 12 months ago
2006
Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
DAGM
2008
Springer
15 years 1 months ago
2008
Springer
Abstract. In this paper, we propose a new approach to learn structured visual compound models from shape-based feature descriptions. We use captioned text in order to drive the pro...
SAC
2009
ACM
15 years 6 months ago
2009
ACM
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...