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AAAI
2008
15 years 11 days ago
Hybrid Markov Logic Networks
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Jue Wang, Pedro Domingos
SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
14 years 11 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
BIB
2007
137views more  BIB 2007»
14 years 10 months ago
Current progress in network research: toward reference networks for key model organisms
The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset t...
Balaji S. Srinivasan, Nigam H. Shah, Jason Flannic...
JFR
2006
88views more  JFR 2006»
14 years 10 months ago
Discovering natural kinds of robot sensory experiences in unstructured environments
We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought betwee...
Daniel H. Grollman, Odest Chadwicke Jenkins, Frank...
PAMI
2008
119views more  PAMI 2008»
14 years 10 months ago
Triplet Markov Fields for the Classification of Complex Structure Data
We address the issue of classifying complex data. We focus on three main sources of complexity, namely, the high dimensionality of the observed data, the dependencies between these...
Juliette Blanchet, Florence Forbes