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JMLR
2006
169views more  JMLR 2006»
13 years 7 months ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
FLAIRS
2006
13 years 9 months ago
Which Dynamic Constraint Problems Can Be Solved By Ants?
There exist a number of algorithms that can solve dynamic constraint satisfaction/optimization problems (DynCSPs or DynCOPs). Because of the large variety in the characteristics o...
Koenraad Mertens, Tom Holvoet, Yolande Berbers
GD
2006
Springer
13 years 11 months ago
Planarity Testing and Optimal Edge Insertion with Embedding Constraints
The planarization method has proven to be successful in graph drawing. The output, a combinatorial planar embedding of the so-called planarized graph, can be combined with state-o...
Carsten Gutwenger, Karsten Klein, Petra Mutzel
JAIR
2010
130views more  JAIR 2010»
13 years 6 months ago
A Constraint Satisfaction Framework for Executing Perceptions and Actions in Diagrammatic Reasoning
Diagrammatic reasoning (DR) is pervasive in human problem solving as a powerful adjunct to symbolic reasoning based on language-like representations. The research reported in this...
Bonny Banerjee, B. Chandrasekaran
CORR
2006
Springer
150views Education» more  CORR 2006»
13 years 7 months ago
The approximability of MAX CSP with fixed-value constraints
In the maximum constraint satisfaction problem (Max CSP), one is given a finite collection of (possibly weighted) constraints on overlapping sets of variables, and the goal is to ...
Vladimir G. Deineko, Peter Jonsson, Mikael Klasson...