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IJCAI
2003
13 years 11 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
SPIRE
2010
Springer
13 years 8 months ago
Why Large Closest String Instances Are Easy to Solve in Practice
We initiate the study of the smoothed complexity of the Closest String problem by proposing a semi-random model of Hamming distance. We restrict interest to the optimization versio...
Christina Boucher, Kathleen Wilkie
NAACL
2010
13 years 7 months ago
An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing
We present a novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner. Traditional d...
Yoav Goldberg, Michael Elhadad
AUTOMATICA
2007
92views more  AUTOMATICA 2007»
13 years 10 months ago
Observability analysis for structured bilinear systems: A graph-theoretic approach
This paper is devoted to the generic observability analysis for structured bilinear systems using a graph-theoretic approach. On the basis of a digraph representation, we express ...
T. Boukhobza, Frédéric Hamelin
PVLDB
2008
182views more  PVLDB 2008»
13 years 9 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...