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» Learning from Highly Structured Data by Decomposition
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IJCAI
2001
15 years 3 months ago
Active Learning for Structure in Bayesian Networks
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments a...
Simon Tong, Daphne Koller
VVS
1996
IEEE
129views Visualization» more  VVS 1996»
15 years 6 months ago
Optimal Isosurface Extraction from Irregular Volume Data
A method is proposed which supports the extraction of isosurfaces from irregular volume data, represented by tetrahedral decomposition, in optimal time. The method is based on a d...
Paolo Cignoni, Claudio Montani, Enrico Puppo, Robe...
ESCIENCE
2007
IEEE
15 years 8 months ago
eResearch Solutions for High Throughput Structural Biology
Structural biology research places significant demands upon computing and informatics infrastructure. Protein production, crystallization and X-ray data collection require solutio...
Noel G. Faux, Anthony Beitz, Mark A. Bate, Abdulla...
SIGIR
2009
ACM
15 years 8 months ago
Extracting structured information from user queries with semi-supervised conditional random fields
When search is against structured documents, it is beneficial to extract information from user queries in a format that is consistent with the backend data structure. As one step...
Xiao Li, Ye-Yi Wang, Alex Acero
JMLR
2002
90views more  JMLR 2002»
15 years 1 months ago
Machine Learning with Data Dependent Hypothesis Classes
We extend the VC theory of statistical learning to data dependent spaces of classifiers. This theory can be viewed as a decomposition of classifier design into two components; the...
Adam Cannon, J. Mark Ettinger, Don R. Hush, Clint ...