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JMLR
2008
150views more  JMLR 2008»
15 years 2 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»
15 years 2 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»
15 years 1 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»
15 years 2 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»
13 years 10 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