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» Classifying Problems into Complexity Classes
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IDEAL
2000
Springer
15 years 10 months ago
Quantization of Continuous Input Variables for Binary Classification
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
Michal Skubacz, Jaakko Hollmén
TON
2008
239views more  TON 2008»
15 years 6 months ago
Characterization of failures in an operational IP backbone network
Abstract--As the Internet evolves into a ubiquitous communication infrastructure and supports increasingly important services, its dependability in the presence of various failures...
Athina Markopoulou, Gianluca Iannaccone, Supratik ...
ICIP
2010
IEEE
15 years 4 months ago
Distributed classification of multiple observations by consensus
We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad-hoc network of vision sensors. Assuming that each sen...
Effrosini Kokiopoulou, Pascal Frossard
SEMWEB
2010
Springer
15 years 4 months ago
Optimising Ontology Classification
Abstract. Ontology classification--the computation of subsumption hierarchies for classes and properties--is one of the most important tasks for OWL reasoners. Based on the algorit...
Birte Glimm, Ian Horrocks, Boris Motik, Giorgos St...
STOC
2004
ACM
150views Algorithms» more  STOC 2004»
16 years 6 months ago
Typical properties of winners and losers in discrete optimization
We present a probabilistic analysis for a large class of combinatorial optimization problems containing, e.g., all binary optimization problems defined by linear constraints and a...
René Beier, Berthold Vöcking