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JMLR
2008
150views more  JMLR 2008»
13 years 9 months ago
Discriminative Learning of Max-Sum Classifiers
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
Vojtech Franc, Bogdan Savchynskyy
NPL
2006
172views more  NPL 2006»
13 years 9 months ago
Adapting RBF Neural Networks to Multi-Instance Learning
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...
Min-Ling Zhang, Zhi-Hua Zhou
SIGCSE
2002
ACM
202views Education» more  SIGCSE 2002»
13 years 9 months ago
A tutorial program for propositional logic with human/computer interactive learning
This paper describes a tutorial program that serves a double role as an educational tool and a research environment. First, it introduces students to fundamental concepts of propo...
Stacy Lukins, Alan Levicki, Jennifer Burg
BMCBI
2007
215views more  BMCBI 2007»
13 years 9 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 5 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