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ICML
2005
IEEE
15 years 12 months ago
Learning first-order probabilistic models with combining rules
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
SIGMOD
2005
ACM
77views Database» more  SIGMOD 2005»
15 years 11 months ago
On Joining and Caching Stochastic Streams
We consider the problem of joining data streams using limited cache memory, with the goal of producing as many result tuples as possible from the cache. Many cache replacement heu...
Jun Yang 0001, Junyi Xie, Yuguo Chen
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 6 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
MOBIHOC
2009
ACM
15 years 11 months ago
Fault tolerant target tracking in sensor networks
In this paper, we present a Gaussian mixture model based approach to capture the spatial characteristics of any target signal in a sensor network, and further propose a temporally...
Min Ding, Xiuzhen Cheng
ML
2000
ACM
124views Machine Learning» more  ML 2000»
14 years 10 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...