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» Logic, Optimization, and Constraint Programming
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JMLR
2012
11 years 10 months ago
Deterministic Annealing for Semi-Supervised Structured Output Learning
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on unlabeled data to deal with the combinatorial nature ...
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Be...
TASLP
2010
78views more  TASLP 2010»
13 years 3 months ago
Solving Demodulation as an Optimization Problem
We introduce two new methods for the demodulation of acoustic signals by posing the problem in a convex optimization framework. This allows the parameters of the modulator and carr...
Gregory Sell, Malcolm Slaney
SIAMJO
2008
99views more  SIAMJO 2008»
13 years 8 months ago
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
Many optimization problems are naturally delivered in an uncertain framework, and one would like to exercise prudence against the uncertainty elements present in the problem. In pr...
Marco C. Campi, Simone Garatti
PPDP
2010
Springer
13 years 6 months ago
Type inference in intuitionistic linear logic
We study the type checking and type inference problems for intuitionistic linear logic: given a System F typed λ-term, (i) for an alleged linear logic type, determine whether the...
Patrick Baillot, Martin Hofmann
ICML
2009
IEEE
14 years 9 months ago
Structure learning of Bayesian networks using constraints
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Cassio Polpo de Campos, Zhi Zeng, Qiang Ji