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CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 2 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
CHI
2009
ACM
16 years 7 months ago
Two studies of opportunistic programming: interleaving web foraging, learning, and writing code
This paper investigates the role of online resources in problem solving. We look specifically at how programmers--an exemplar form of knowledge workers--opportunistically interlea...
Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira ...
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
16 years 7 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
VLDB
2003
ACM
165views Database» more  VLDB 2003»
16 years 6 months ago
Learning to match ontologies on the Semantic Web
On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings be...
AnHai Doan, Jayant Madhavan, Robin Dhamankar, Pedr...
PREMI
2005
Springer
15 years 12 months ago
Geometric Decision Rules for Instance-Based Learning Problems
In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a d...
Binay K. Bhattacharya, Kaustav Mukherjee, Godfried...